.env.example

@@ -1,272 +0,0 @@
-#################
-# GENERAL
-#################
-module_name=dronedetector
-freqs=433,750,868,915,1200,1500,2400,3300,4500,5200,5800
-
-signal_threshold=0.02
-signal_threshold_433=0.0195
-signal_threshold_700=0.01
-signal_threshold_868=0.01
-signal_threshold_5200=0.01
-signal_threshold_5800=0.01
-
-# Runtime flags
-# 1/0, true/false, yes/no, on/off
-debug_flag=0
-debug_freq_flag=0
-debug_module_flag=0
-send_to_module_flag=1
-save_data_flag=0
-freq_endpoint=process_data
-send_to_master_flag=1
-send_to_jammer_flag=0
-jammer_timeout=2
-master_timeout=1
-amount_connection_attempts=3
-mad_k_on=5.0
-mad_eps=0.05
-# Per-frequency MAD sensitivity (scanner frequencies)
-
-path_to_save_medians=/tmp/dronedetector/noises_medians/
-path_to_save_alarms=/tmp/dronedetector/alarms/
-elems_to_save=signal,abs_signal
-file_types_to_save=npy,npy
-smb_host=127.0.0.1
-smb_port=139
-smb_user=guest
-smb_pass=guest
-shared_folder=shared
-the_pc_name=dronedetector-host
-remote_pc_name=dronedetector-storage
-smb_domain=WORKGROUP
-
-#################
-# 433
-#################
-hack_433=
-c_freq_433=433
-f_step_433=-20e6
-f_bases_433=0.480e9
-f_roofs_433=0.400e9
-signal_length_433=1000000
-buffer_columns_size_433=11
-num_of_thinning_iter_433=32
-multiply_factor_433=1.3
-num_for_alarm_433=4
-mad_k_on_433=0
-dbfs_linear_offset_db_433=0
-dbfs_linear_abs_median_scale_433=0
-
-#################
-# 750
-#################
-hack_750=
-c_freq_750=750
-f_step_750=-20e6
-f_bases_750=0.755e9
-f_roofs_750=0.695e9
-signal_length_750=1000000
-buffer_columns_size_750=11
-num_of_thinning_iter_750=32
-multiply_factor_750=1.3
-num_for_alarm_750=4
-mad_k_on_750=0
-dbfs_linear_offset_db_750=0
-dbfs_linear_abs_median_scale_750=0
-
-
-#################
-# 915
-#################
-hack_915=
-c_freq_915=915
-f_step_915=-20e6
-f_bases_915=0.98e9
-f_roofs_915=0.840e9
-signal_length_915=1000000
-buffer_columns_size_915=11
-num_of_thinning_iter_915=32
-multiply_factor_915=1.3
-num_for_alarm_915=4
-mad_k_on_915=0
-dbfs_linear_offset_db_915=0
-dbfs_linear_abs_median_scale_915=0
-
-#################
-# 1200
-#################
-hack_1200=
-c_freq_1200=1200
-f_step_1200=-20e6
-f_bases_1200=1.1e9
-f_roofs_1200=1.3e9
-signal_length_1200=1000000
-buffer_columns_size_1200=11
-num_of_thinning_iter_1200=32
-multiply_factor_1200=1.3
-num_for_alarm_1200=4
-mad_k_on_1200=0
-dbfs_linear_offset_db_1200=0
-dbfs_linear_abs_median_scale_1200=0
-
-
-#################
-# 1500
-#################
-hack_1500=
-c_freq_1500=1500
-f_step_1500=-20e6
-f_bases_1500=1.605e9
-f_roofs_1500=1.405e9
-signal_length_1500=1000000
-buffer_columns_size_1500=11
-num_of_thinning_iter_1500=32
-multiply_factor_1500=1.3
-num_for_alarm_1500=4
-mad_k_on_1500=0
-dbfs_linear_offset_db_1500=0
-dbfs_linear_abs_median_scale_1500=0
-
-
-#################
-# 2400
-#################
-hack_2400=
-c_freq_2400=2400
-f_step_2400=-20e6
-f_bases_2400=2.3e9
-f_roofs_2400=2.5e9
-signal_length_2400=1000000
-buffer_columns_size_2400=11
-num_of_thinning_iter_2400=32
-multiply_factor_2400=1.3
-num_for_alarm_2400=4
-mad_k_on_2400=0
-dbfs_linear_offset_db_2400=0
-dbfs_linear_abs_median_scale_2400=0
-
-#################
-# 3300
-#################
-hack_3300=
-c_freq_3300=3300
-f_step_3300=-20e6
-f_bases_3300=3.5e9
-f_roofs_3300=3.3e9
-signal_length_3300=1000000
-buffer_columns_size_3300=11
-num_of_thinning_iter_3300=32
-multiply_factor_3300=1.3
-num_for_alarm_3300=3
-mad_k_on_3300=0
-dbfs_linear_offset_db_3300=0
-dbfs_linear_abs_median_scale_3300=0
-
-#################
-# 4500
-#################
-hack_4500=
-c_freq_4500=4500
-f_step_4500=-20e6
-f_bases_4500=4.7e9
-f_roofs_4500=4.5e9
-signal_length_4500=1000000
-buffer_columns_size_4500=11
-num_of_thinning_iter_4500=32
-multiply_factor_4500=1.3
-num_for_alarm_4500=2
-mad_k_on_4500=0
-dbfs_linear_offset_db_4500=0
-dbfs_linear_abs_median_scale_4500=0
-
-
-#################
-# 5200
-#################
-hack_5200=
-c_freq_5200=5200
-f_step_5200=-20e6
-f_bases_5200=5.3e9
-f_roofs_5200=5.1e9
-signal_length_5200=1000000
-buffer_columns_size_5200=11
-num_of_thinning_iter_5200=32
-multiply_factor_5200=1.08
-num_for_alarm_5200=3
-mad_k_on_5200=0
-dbfs_linear_offset_db_5200=0
-dbfs_linear_abs_median_scale_4500=0
-
-#################
-# 5800
-#################
-hack_5800=
-c_freq_5800=5800
-f_step_5800=-20e6
-f_bases_5800=5.8e9
-f_roofs_5800=5.5e9
-signal_length_5800=1000000
-buffer_columns_size_5800=11
-num_of_thinning_iter_5800=32
-multiply_factor_5800=1.08
-num_for_alarm_5800=3
-mad_k_on_5800=0
-dbfs_linear_offset_db_5200=0
-dbfs_linear_abs_median_scale_5800=0
-
-
-#################
-# MODULE SERVER (server_to_master)
-#################
-lochost=0.0.0.0
-locport=5010
-master_server_ip=192.168.11.223
-master_server_port=3000
-module_mac=bc:fc:e7:ca:87:2b
-module_ip=192.168.11.223
-threshold_to_alarm=5
-num_of_clear_packs=5
-time_to_jam=30
-time_to_fresh=5
-active_interval_to_send=1
-passive_interval_to_send=60
-gpsport=/dev/null
-latitude=1
-longitude=1
-jamhost=127.0.0.1
-jamport=3000
-
-#################
-# ORANGE SDR (915/1200/2400 -> NN_server)
-#################
-POROG_2400=0.01
-HACKID_2400=
-POROG_1200=0.01
-HACKID_1200=
-POROG_915=0.01
-HACKID_915=
-SERVER_IP_1=127.0.0.1
-SERVER_PORT_1=8080
-SERVER_IP_2=127.0.0.1
-SERVER_PORT_2=8080
-
-#################
-# NN_SERVER
-#################
-FREQS=915,1200,2400
-PATH_TO_NN=/app/NN_server/NN/
-SRC_RESULT=/app/NN_server/result/
-SRC_EXAMPLE=${PATH_TO_NN}example/
-
-NN_1='${PATH_TO_NN}resnet18_1.pth && ${PATH_TO_NN}config_resnet18.yaml && ${SRC_EXAMPLE} && ${SRC_RESULT} && Resnet18_1_2400 && build_func_resnet18 && pre_func_resnet18 && inference_func_resnet18 && post_func_resnet18 && [drone,noise,wifi] && 10 && 1 && /app/NN_server/datasets/full_dataset_pic/'
-NN_21='${PATH_TO_NN}ensemble_1.2.pth && ${PATH_TO_NN}config_ensemble.yaml && ${SRC_EXAMPLE} && ${SRC_RESULT} && ensemble_1200 && build_func_ensemble && pre_func_ensemble && inference_func_ensemble && post_func_ensemble && [drone,noise] && 10 && 1 && /app/NN_server/datasets/full_dataset/'
-NN_22='${PATH_TO_NN}ensemble_915.pth && ${PATH_TO_NN}config_ensemble.yaml && ${SRC_EXAMPLE} && ${SRC_RESULT} && ensemble_915 && build_func_ensemble && pre_func_ensemble && inference_func_ensemble && post_func_ensemble && [drone,noise] && 10 && 1 && /app/NN_server/datasets/full_dataset/'
-
-GENERAL_SERVER_IP=dronedetector-server-to-master
-GENERAL_SERVER_PORT=5010
-SERVER_IP=0.0.0.0
-SERVER_PORT=8080
-
-NN_HOT_RELOAD=1
-

.gitignore

@@ -147,7 +147,7 @@ celerybeat.pid
 .env.bak*
 .venv
 env/
-.venv/
+venv/
 ENV/
 env.bak/
 venv.bak/

.venv-sdr/bin/Activate.ps1

@@ -0,0 +1,247 @@
+<#
+.Synopsis
+Activate a Python virtual environment for the current PowerShell session.
+
+.Description
+Pushes the python executable for a virtual environment to the front of the
+$Env:PATH environment variable and sets the prompt to signify that you are
+in a Python virtual environment. Makes use of the command line switches as
+well as the `pyvenv.cfg` file values present in the virtual environment.
+
+.Parameter VenvDir
+Path to the directory that contains the virtual environment to activate. The
+default value for this is the parent of the directory that the Activate.ps1
+script is located within.
+
+.Parameter Prompt
+The prompt prefix to display when this virtual environment is activated. By
+default, this prompt is the name of the virtual environment folder (VenvDir)
+surrounded by parentheses and followed by a single space (ie. '(.venv) ').
+
+.Example
+Activate.ps1
+Activates the Python virtual environment that contains the Activate.ps1 script.
+
+.Example
+Activate.ps1 -Verbose
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and shows extra information about the activation as it executes.
+
+.Example
+Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv
+Activates the Python virtual environment located in the specified location.
+
+.Example
+Activate.ps1 -Prompt "MyPython"
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and prefixes the current prompt with the specified string (surrounded in
+parentheses) while the virtual environment is active.
+
+.Notes
+On Windows, it may be required to enable this Activate.ps1 script by setting the
+execution policy for the user. You can do this by issuing the following PowerShell
+command:
+
+PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
+
+For more information on Execution Policies: 
+https://go.microsoft.com/fwlink/?LinkID=135170
+
+#>
+Param(
+    [Parameter(Mandatory = $false)]
+    [String]
+    $VenvDir,
+    [Parameter(Mandatory = $false)]
+    [String]
+    $Prompt
+)
+
+<# Function declarations --------------------------------------------------- #>
+
+<#
+.Synopsis
+Remove all shell session elements added by the Activate script, including the
+addition of the virtual environment's Python executable from the beginning of
+the PATH variable.
+
+.Parameter NonDestructive
+If present, do not remove this function from the global namespace for the
+session.
+
+#>
+function global:deactivate ([switch]$NonDestructive) {
+    # Revert to original values
+
+    # The prior prompt:
+    if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) {
+        Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt
+        Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT
+    }
+
+    # The prior PYTHONHOME:
+    if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) {
+        Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME
+        Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME
+    }
+
+    # The prior PATH:
+    if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) {
+        Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH
+        Remove-Item -Path Env:_OLD_VIRTUAL_PATH
+    }
+
+    # Just remove the VIRTUAL_ENV altogether:
+    if (Test-Path -Path Env:VIRTUAL_ENV) {
+        Remove-Item -Path env:VIRTUAL_ENV
+    }
+
+    # Just remove VIRTUAL_ENV_PROMPT altogether.
+    if (Test-Path -Path Env:VIRTUAL_ENV_PROMPT) {
+        Remove-Item -Path env:VIRTUAL_ENV_PROMPT
+    }
+
+    # Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether:
+    if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) {
+        Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force
+    }
+
+    # Leave deactivate function in the global namespace if requested:
+    if (-not $NonDestructive) {
+        Remove-Item -Path function:deactivate
+    }
+}
+
+<#
+.Description
+Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the
+given folder, and returns them in a map.
+
+For each line in the pyvenv.cfg file, if that line can be parsed into exactly
+two strings separated by `=` (with any amount of whitespace surrounding the =)
+then it is considered a `key = value` line. The left hand string is the key,
+the right hand is the value.
+
+If the value starts with a `'` or a `"` then the first and last character is
+stripped from the value before being captured.
+
+.Parameter ConfigDir
+Path to the directory that contains the `pyvenv.cfg` file.
+#>
+function Get-PyVenvConfig(
+    [String]
+    $ConfigDir
+) {
+    Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg"
+
+    # Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue).
+    $pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue
+
+    # An empty map will be returned if no config file is found.
+    $pyvenvConfig = @{ }
+
+    if ($pyvenvConfigPath) {
+
+        Write-Verbose "File exists, parse `key = value` lines"
+        $pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath
+
+        $pyvenvConfigContent | ForEach-Object {
+            $keyval = $PSItem -split "\s*=\s*", 2
+            if ($keyval[0] -and $keyval[1]) {
+                $val = $keyval[1]
+
+                # Remove extraneous quotations around a string value.
+                if ("'""".Contains($val.Substring(0, 1))) {
+                    $val = $val.Substring(1, $val.Length - 2)
+                }
+
+                $pyvenvConfig[$keyval[0]] = $val
+                Write-Verbose "Adding Key: '$($keyval[0])'='$val'"
+            }
+        }
+    }
+    return $pyvenvConfig
+}
+
+
+<# Begin Activate script --------------------------------------------------- #>
+
+# Determine the containing directory of this script
+$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition
+$VenvExecDir = Get-Item -Path $VenvExecPath
+
+Write-Verbose "Activation script is located in path: '$VenvExecPath'"
+Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)"
+Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)"
+
+# Set values required in priority: CmdLine, ConfigFile, Default
+# First, get the location of the virtual environment, it might not be
+# VenvExecDir if specified on the command line.
+if ($VenvDir) {
+    Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values"
+}
+else {
+    Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir."
+    $VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/")
+    Write-Verbose "VenvDir=$VenvDir"
+}
+
+# Next, read the `pyvenv.cfg` file to determine any required value such
+# as `prompt`.
+$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir
+
+# Next, set the prompt from the command line, or the config file, or
+# just use the name of the virtual environment folder.
+if ($Prompt) {
+    Write-Verbose "Prompt specified as argument, using '$Prompt'"
+}
+else {
+    Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value"
+    if ($pyvenvCfg -and $pyvenvCfg['prompt']) {
+        Write-Verbose "  Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'"
+        $Prompt = $pyvenvCfg['prompt'];
+    }
+    else {
+        Write-Verbose "  Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virtual environment)"
+        Write-Verbose "  Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'"
+        $Prompt = Split-Path -Path $venvDir -Leaf
+    }
+}
+
+Write-Verbose "Prompt = '$Prompt'"
+Write-Verbose "VenvDir='$VenvDir'"
+
+# Deactivate any currently active virtual environment, but leave the
+# deactivate function in place.
+deactivate -nondestructive
+
+# Now set the environment variable VIRTUAL_ENV, used by many tools to determine
+# that there is an activated venv.
+$env:VIRTUAL_ENV = $VenvDir
+
+if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) {
+
+    Write-Verbose "Setting prompt to '$Prompt'"
+
+    # Set the prompt to include the env name
+    # Make sure _OLD_VIRTUAL_PROMPT is global
+    function global:_OLD_VIRTUAL_PROMPT { "" }
+    Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT
+    New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt
+
+    function global:prompt {
+        Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) "
+        _OLD_VIRTUAL_PROMPT
+    }
+    $env:VIRTUAL_ENV_PROMPT = $Prompt
+}
+
+# Clear PYTHONHOME
+if (Test-Path -Path Env:PYTHONHOME) {
+    Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME
+    Remove-Item -Path Env:PYTHONHOME
+}
+
+# Add the venv to the PATH
+Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH
+$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH"

.venv-sdr/bin/activate

@@ -0,0 +1,70 @@
+# This file must be used with "source bin/activate" *from bash*
+# You cannot run it directly
+
+deactivate () {
+    # reset old environment variables
+    if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then
+        PATH="${_OLD_VIRTUAL_PATH:-}"
+        export PATH
+        unset _OLD_VIRTUAL_PATH
+    fi
+    if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then
+        PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}"
+        export PYTHONHOME
+        unset _OLD_VIRTUAL_PYTHONHOME
+    fi
+
+    # Call hash to forget past commands. Without forgetting
+    # past commands the $PATH changes we made may not be respected
+    hash -r 2> /dev/null
+
+    if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then
+        PS1="${_OLD_VIRTUAL_PS1:-}"
+        export PS1
+        unset _OLD_VIRTUAL_PS1
+    fi
+
+    unset VIRTUAL_ENV
+    unset VIRTUAL_ENV_PROMPT
+    if [ ! "${1:-}" = "nondestructive" ] ; then
+    # Self destruct!
+        unset -f deactivate
+    fi
+}
+
+# unset irrelevant variables
+deactivate nondestructive
+
+# on Windows, a path can contain colons and backslashes and has to be converted:
+if [ "${OSTYPE:-}" = "cygwin" ] || [ "${OSTYPE:-}" = "msys" ] ; then
+    # transform D:\path\to\venv to /d/path/to/venv on MSYS
+    # and to /cygdrive/d/path/to/venv on Cygwin
+    export VIRTUAL_ENV=$(cygpath /home/sibscience-4/from_ssh/DroneDetector/.venv-sdr)
+else
+    # use the path as-is
+    export VIRTUAL_ENV=/home/sibscience-4/from_ssh/DroneDetector/.venv-sdr
+fi
+
+_OLD_VIRTUAL_PATH="$PATH"
+PATH="$VIRTUAL_ENV/"bin":$PATH"
+export PATH
+
+# unset PYTHONHOME if set
+# this will fail if PYTHONHOME is set to the empty string (which is bad anyway)
+# could use `if (set -u; : $PYTHONHOME) ;` in bash
+if [ -n "${PYTHONHOME:-}" ] ; then
+    _OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}"
+    unset PYTHONHOME
+fi
+
+if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then
+    _OLD_VIRTUAL_PS1="${PS1:-}"
+    PS1='(.venv-sdr) '"${PS1:-}"
+    export PS1
+    VIRTUAL_ENV_PROMPT='(.venv-sdr) '
+    export VIRTUAL_ENV_PROMPT
+fi
+
+# Call hash to forget past commands. Without forgetting
+# past commands the $PATH changes we made may not be respected
+hash -r 2> /dev/null

.venv-sdr/bin/activate.csh

@@ -0,0 +1,27 @@
+# This file must be used with "source bin/activate.csh" *from csh*.
+# You cannot run it directly.
+
+# Created by Davide Di Blasi <davidedb@gmail.com>.
+# Ported to Python 3.3 venv by Andrew Svetlov <andrew.svetlov@gmail.com>
+
+alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; unsetenv VIRTUAL_ENV_PROMPT; test "\!:*" != "nondestructive" && unalias deactivate'
+
+# Unset irrelevant variables.
+deactivate nondestructive
+
+setenv VIRTUAL_ENV /home/sibscience-4/from_ssh/DroneDetector/.venv-sdr
+
+set _OLD_VIRTUAL_PATH="$PATH"
+setenv PATH "$VIRTUAL_ENV/"bin":$PATH"
+
+
+set _OLD_VIRTUAL_PROMPT="$prompt"
+
+if (! "$?VIRTUAL_ENV_DISABLE_PROMPT") then
+    set prompt = '(.venv-sdr) '"$prompt"
+    setenv VIRTUAL_ENV_PROMPT '(.venv-sdr) '
+endif
+
+alias pydoc python -m pydoc
+
+rehash

.venv-sdr/bin/activate.fish

@@ -0,0 +1,69 @@
+# This file must be used with "source <venv>/bin/activate.fish" *from fish*
+# (https://fishshell.com/). You cannot run it directly.
+
+function deactivate  -d "Exit virtual environment and return to normal shell environment"
+    # reset old environment variables
+    if test -n "$_OLD_VIRTUAL_PATH"
+        set -gx PATH $_OLD_VIRTUAL_PATH
+        set -e _OLD_VIRTUAL_PATH
+    end
+    if test -n "$_OLD_VIRTUAL_PYTHONHOME"
+        set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME
+        set -e _OLD_VIRTUAL_PYTHONHOME
+    end
+
+    if test -n "$_OLD_FISH_PROMPT_OVERRIDE"
+        set -e _OLD_FISH_PROMPT_OVERRIDE
+        # prevents error when using nested fish instances (Issue #93858)
+        if functions -q _old_fish_prompt
+            functions -e fish_prompt
+            functions -c _old_fish_prompt fish_prompt
+            functions -e _old_fish_prompt
+        end
+    end
+
+    set -e VIRTUAL_ENV
+    set -e VIRTUAL_ENV_PROMPT
+    if test "$argv[1]" != "nondestructive"
+        # Self-destruct!
+        functions -e deactivate
+    end
+end
+
+# Unset irrelevant variables.
+deactivate nondestructive
+
+set -gx VIRTUAL_ENV /home/sibscience-4/from_ssh/DroneDetector/.venv-sdr
+
+set -gx _OLD_VIRTUAL_PATH $PATH
+set -gx PATH "$VIRTUAL_ENV/"bin $PATH
+
+# Unset PYTHONHOME if set.
+if set -q PYTHONHOME
+    set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME
+    set -e PYTHONHOME
+end
+
+if test -z "$VIRTUAL_ENV_DISABLE_PROMPT"
+    # fish uses a function instead of an env var to generate the prompt.
+
+    # Save the current fish_prompt function as the function _old_fish_prompt.
+    functions -c fish_prompt _old_fish_prompt
+
+    # With the original prompt function renamed, we can override with our own.
+    function fish_prompt
+        # Save the return status of the last command.
+        set -l old_status $status
+
+        # Output the venv prompt; color taken from the blue of the Python logo.
+        printf "%s%s%s" (set_color 4B8BBE) '(.venv-sdr) ' (set_color normal)
+
+        # Restore the return status of the previous command.
+        echo "exit $old_status" | .
+        # Output the original/"old" prompt.
+        _old_fish_prompt
+    end
+
+    set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV"
+    set -gx VIRTUAL_ENV_PROMPT '(.venv-sdr) '
+end

.venv-sdr/bin/f2py

@@ -0,0 +1,6 @@
+#!/home/sibscience-4/from_ssh/DroneDetector/.venv-sdr/bin/python3
+import sys
+from numpy.f2py.f2py2e import main
+if __name__ == '__main__':
+    sys.argv[0] = sys.argv[0].removesuffix('.exe')
+    sys.exit(main())

.venv-sdr/bin/normalizer

@@ -0,0 +1,6 @@
+#!/home/sibscience-4/from_ssh/DroneDetector/.venv-sdr/bin/python3
+import sys
+from charset_normalizer.cli import cli_detect
+if __name__ == '__main__':
+    sys.argv[0] = sys.argv[0].removesuffix('.exe')
+    sys.exit(cli_detect())

.venv-sdr/bin/pip

@@ -0,0 +1,8 @@
+#!/home/sibscience-4/from_ssh/DroneDetector/.venv-sdr/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from pip._internal.cli.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

.venv-sdr/bin/pip3

@@ -0,0 +1,8 @@
+#!/home/sibscience-4/from_ssh/DroneDetector/.venv-sdr/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from pip._internal.cli.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

.venv-sdr/bin/pip3.12

@@ -0,0 +1,8 @@
+#!/home/sibscience-4/from_ssh/DroneDetector/.venv-sdr/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from pip._internal.cli.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

.venv-sdr/bin/python

@@ -0,0 +1 @@
+python3

.venv-sdr/bin/python3

@@ -0,0 +1 @@
+/usr/bin/python3

.venv-sdr/bin/python3.12

@@ -0,0 +1 @@
+python3

.venv-sdr/lib64

@@ -0,0 +1 @@
+lib

.venv-sdr/pyvenv.cfg

@@ -0,0 +1,5 @@
+home = /usr/bin
+include-system-site-packages = true
+version = 3.12.3
+executable = /usr/bin/python3.12
+command = /usr/bin/python3 -m venv --system-site-packages /home/sibscience-4/from_ssh/DroneDetector/.venv-sdr

CLAUDE.md

@@ -1,66 +0,0 @@
-
-# CLAUDE.md
-
-Behavioral guidelines to reduce common LLM coding mistakes. Merge with project-specific instructions as needed.
-
-**Tradeoff:** These guidelines bias toward caution over speed. For trivial tasks, use judgment.
-
-## 1. Think Before Coding
-
-**Don't assume. Don't hide confusion. Surface tradeoffs.**
-
-Before implementing:
-- State your assumptions explicitly. If uncertain, ask.
-- If multiple interpretations exist, present them - don't pick silently.
-- If a simpler approach exists, say so. Push back when warranted.
-- If something is unclear, stop. Name what's confusing. Ask.
-
-## 2. Simplicity First
-
-**Minimum code that solves the problem. Nothing speculative.**
-
-- No features beyond what was asked.
-- No abstractions for single-use code.
-- No "flexibility" or "configurability" that wasn't requested.
-- No error handling for impossible scenarios.
-- If you write 200 lines and it could be 50, rewrite it.
-
-Ask yourself: "Would a senior engineer say this is overcomplicated?" If yes, simplify.
-
-## 3. Surgical Changes
-
-**Touch only what you must. Clean up only your own mess.**
-
-When editing existing code:
-- Don't "improve" adjacent code, comments, or formatting.
-- Don't refactor things that aren't broken.
-- Match existing style, even if you'd do it differently.
-- If you notice unrelated dead code, mention it - don't delete it.
-
-When your changes create orphans:
-- Remove imports/variables/functions that YOUR changes made unused.
-- Don't remove pre-existing dead code unless asked.
-
-The test: Every changed line should trace directly to the user's request.
-
-## 4. Goal-Driven Execution
-
-**Define success criteria. Loop until verified.**
-
-Transform tasks into verifiable goals:
-- "Add validation" → "Write tests for invalid inputs, then make them pass"
-- "Fix the bug" → "Write a test that reproduces it, then make it pass"
-- "Refactor X" → "Ensure tests pass before and after"
-
-For multi-step tasks, state a brief plan:
-```
-1. [Step] → verify: [check]
-2. [Step] → verify: [check]
-3. [Step] → verify: [check]
-```
-
-Strong success criteria let you loop independently. Weak criteria ("make it work") require constant clarification.
-
----
-
-**These guidelines are working if:** fewer unnecessary changes in diffs, fewer rewrites due to overcomplication, and clarifying questions come before implementation rather than after mistakes.

README.md

@@ -173,7 +173,8 @@ sudo systemctl stop dronedetector-sdr-5800.service
 sudo systemctl stop dronedetector-sdr-1200.service
 sudo systemctl stop dronedetector-sdr-2400.service
 sudo systemctl stop dronedetector-sdr-1500.service
-sudo systemctl stop dronedetector-sdr-915.service
+sudo systemctl stop dronedetector-sdr-868.service
+sudo systemctl stop dronedetector-sdr-868-915.service
 ```
 
 
@@ -197,6 +198,8 @@ since="$(systemctl show -p ActiveEnterTimestamp --value dronedetector-sdr-5800.s
 sudo journalctl -u dronedetector-sdr-5800.service --since "$since" --no-pager
 ```
 
+
+
 ### Просмотр логов от server-to-master   
 ``` bash
 docker compose -f deploy/docker/docker-compose.yml logs --timestamps dronedetector-server-to-master | tail -n 50
@@ -207,24 +210,4 @@ docker compose -f deploy/docker/docker-compose.yml logs --timestamps dronedetect
 sudo hackrf_spiflash -w hackrf_one_usb.bin
 ```
 
-### Wide_read_energy
-``` bash
-./.venv-sdr/bin/python read_energy_wide.py \
-    --serial 0000000000000000a18c63dc2a83b813 \
-    --sample-rate 20000000 \
-    --base 6000 \
-    --roof 5700 \
-    --step 20
-```
-
-### Проверка тревоги 
-``` bash
-curl -X POST 'http://127.0.0.1:5010/process_data' \
-    -H 'Content-Type: application/json' \
-    -d '{"freq":"1200","amplitude":9}'
-```
-
-### Read_energy
-``` bash 
-.venv-sdr/bin/python read_energy.py 
-```
+./.venv-sdr/bin/python scripts_nn/data_saver_headless.py   --serial 000  --freq 4500000000   --save-dir /home/sibscience-4/Dataset/3300   --file-tag DJI_3_   --samp-rate 20000000   --split-size 400000   --delay 0.1   --rf-gain 12   --if-gain 30   --bb-gain 36

capture_hourly.sh

@@ -1,44 +1,31 @@
 #!/usr/bin/env bash
 set -Eeuo pipefail
 
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-SCRIPT_OWNER="${CAPTURE_USER:-$(stat -c %U "$SCRIPT_DIR")}"
-SCRIPT_OWNER_HOME="$(getent passwd "$SCRIPT_OWNER" | cut -d: -f6)"
-cd "$SCRIPT_DIR"
-
-source "$SCRIPT_DIR/.env"
 ############################
 # НАСТРОЙКИ
 ############################
 
-BASE_DIR="${CAPTURE_BASE_DIR:-${SCRIPT_OWNER_HOME}/dataset/noise}"
+# ЗАМЕНИ на реальную точку монтирования nvme1n1
+BASE_DIR="/mnt/nvme1/dataset"
 
 # Путь к python из venv
-PYTHON_BIN="${PYTHON_BIN:-$SCRIPT_DIR/.venv-sdr/bin/python}"
+PYTHON_BIN="${PYTHON_BIN:-$PWD/.venv-sdr/bin/python}"
 
 # Путь к headless скрипту
-SCRIPT_PATH="${SCRIPT_PATH:-$SCRIPT_DIR/scripts_nn/data_saver_headless.py}"
-
-RUN_ONCE="${RUN_ONCE:-0}"
-CAPTURE_LOG_FILE="${CAPTURE_LOG_FILE:-$BASE_DIR/capture_hourly.log}"
-
-SYSTEMCTL_BIN=(systemctl)
-CURRENT_CAPTURE_PID=""
-CURRENT_SERVICE_UNIT=""
-
+SCRIPT_PATH="${SCRIPT_PATH:-$PWD/scripts_nn/data_saver_headless.py}"
 
+# Проверка, что каталог реально на nvme1n1
+EXPECTED_DEVICE_PREFIX="/dev/nvme1n1"
 
 # Лимиты
-
-lim_all=10
-PER_FREQ_LIMIT_BYTES=$((1 * 1024 * 1024 * 1024))     #  GiB на частоту за запуск
-TOTAL_LIMIT_BYTES=$((lim_all * 1024 * 1024 * 1024))      # общий лимит  GiB
-CYCLE_SECONDS=1                               # один цикл в час
+PER_FREQ_LIMIT_BYTES=$((3 * 1024 * 1024 * 1024))     # 3 GiB на частоту за запуск
+TOTAL_LIMIT_BYTES=$((128 * 1024 * 1024 * 1024))      # общий лимит 512 GiB
+CYCLE_SECONDS=36                                # один цикл в час
 
 # Параметры SDR
 SAMP_RATE="20e6"
 SPLIT_SIZE="400000"
-DELAY="0.01"
+DELAY="0.1"
 RF_GAIN="12"
 IF_GAIN="30"
 BB_GAIN="36"
@@ -47,51 +34,41 @@ BB_GAIN="36"
 # ЧАСТОТЫ И SERIAL ИЗ ENV
 ############################
 
-ORDER=(433)
-
+ORDER=(433 750 915 1200 2400 3300 4500 5200 5800)
 
 declare -A SERIAL
 declare -A FREQ_HZ
-declare -A SERVICE_UNIT
 
-SERIAL[433]="$hack_433"
+SERIAL[433]="0000000000000000114864dc382a8e1b"
 FREQ_HZ[433]="433000000"
 
-SERIAL[750]="$hack_750"
+SERIAL[750]="0000000000000000114864dc383d0d1b"
 FREQ_HZ[750]="750000000"
 
+SERIAL[868]="000000000000000026a468dc36066b63"
+FREQ_HZ[868]="868000000"
 
-SERIAL[915]="$hack_915"
+SERIAL[915]="000000000000000026a468dc36066b63"
 FREQ_HZ[915]="915000000"
 
-SERIAL[1200]="$hack_1200"
+SERIAL[1200]="0000000000000000518864dc32660d83"
 FREQ_HZ[1200]="1200000000"
 
-SERIAL[2400]="$hack_2400"
+SERIAL[2400]="0000000000000000919068dc3437b31f"
 FREQ_HZ[2400]="2400000000"
 
-SERIAL[3300]="$hack_3300"
+SERIAL[3300]="0000000000000000114864dc325069a3"
 FREQ_HZ[3300]="3300000000"
 
-SERIAL[4500]="$hack_4500"
+SERIAL[4500]="0000000000000000518864dc33833183"
 FREQ_HZ[4500]="4500000000"
 
-SERIAL[5200]="$hack_5200"
+SERIAL[5200]="0000000000000000874461dc241c7857"
 FREQ_HZ[5200]="5200000000"
 
-SERIAL[5800]="$hack_5800"
+SERIAL[5800]="0000000000000000919068dc35781c1f"
 FREQ_HZ[5800]="5800000000"
 
-SERVICE_UNIT[433]="dronedetector-sdr-433.service"
-SERVICE_UNIT[750]="dronedetector-sdr-750.service"
-SERVICE_UNIT[915]="dronedetector-sdr-915.service"
-SERVICE_UNIT[1200]="dronedetector-sdr-1200.service"
-SERVICE_UNIT[2400]="dronedetector-sdr-2400.service"
-SERVICE_UNIT[3300]="dronedetector-sdr-3300.service"
-SERVICE_UNIT[4500]="dronedetector-sdr-4500.service"
-SERVICE_UNIT[5200]="dronedetector-sdr-5200.service"
-SERVICE_UNIT[5800]="dronedetector-sdr-5800.service"
-
 ############################
 # ВСПОМОГАТЕЛЬНОЕ
 ############################
@@ -100,65 +77,6 @@ log() {
   printf '[%s] %s\n' "$(date '+%F %T')" "$*"
 }
 
-systemctl_run() {
-  "${SYSTEMCTL_BIN[@]}" "$@"
-}
-
-service_exists() {
-  local unit="$1"
-  systemctl_run show "$unit" >/dev/null 2>&1
-}
-
-stop_band_service() {
-  local band="$1"
-  local unit="${SERVICE_UNIT[$band]:-}"
-
-  if [[ -z "$unit" ]]; then
-    log "Для band=$band не найден service unit"
-    return 0
-  fi
-
-  if ! service_exists "$unit"; then
-    log "Service unit $unit не установлен, пропускаю stop/start"
-    return 0
-  fi
-
-  log "Останавливаю service $unit перед записью band=$band"
-  systemctl_run stop "$unit"
-  CURRENT_SERVICE_UNIT="$unit"
-}
-
-start_current_service() {
-  if [[ -z "$CURRENT_SERVICE_UNIT" ]]; then
-    return 0
-  fi
-
-  log "Запускаю service $CURRENT_SERVICE_UNIT после записи"
-  systemctl_run start "$CURRENT_SERVICE_UNIT"
-  CURRENT_SERVICE_UNIT=""
-}
-
-cleanup_capture() {
-  if [[ -n "$CURRENT_CAPTURE_PID" ]] && kill -0 "$CURRENT_CAPTURE_PID" 2>/dev/null; then
-    log "Останавливаю текущий PID=$CURRENT_CAPTURE_PID при завершении скрипта"
-    kill -TERM "$CURRENT_CAPTURE_PID" 2>/dev/null || true
-    wait "$CURRENT_CAPTURE_PID" 2>/dev/null || true
-  fi
-  CURRENT_CAPTURE_PID=""
-
-  if [[ -n "$CURRENT_SERVICE_UNIT" ]]; then
-    log "Восстанавливаю service $CURRENT_SERVICE_UNIT при завершении скрипта"
-    systemctl_run start "$CURRENT_SERVICE_UNIT" || true
-    CURRENT_SERVICE_UNIT=""
-  fi
-}
-
-on_signal() {
-  local sig="$1"
-  log "Получен сигнал $sig, завершаю работу"
-  exit 130
-}
-
 dir_size_bytes() {
   local path="$1"
   if [[ -e "$path" ]]; then
@@ -183,41 +101,16 @@ ensure_requirements() {
     exit 1
   fi
 
-  if ! command -v systemctl >/dev/null 2>&1; then
-    echo "Не найден systemctl" >&2
-    exit 1
-  fi
-
-  if [[ "${EUID}" -ne 0 ]]; then
-    if command -v sudo >/dev/null 2>&1; then
-      SYSTEMCTL_BIN=(sudo systemctl)
-    else
-      echo "Для управления service нужен root или sudo" >&2
-      exit 1
-    fi
-  fi
-
   mkdir -p "$BASE_DIR"
-  mkdir -p "$(dirname "$CAPTURE_LOG_FILE")"
 
-  if [[ -n "${CAPTURE_ORDER:-}" ]]; then
-    ORDER=()
-    local band
-    for band in ${CAPTURE_ORDER//,/ }; do
-      ORDER+=("$band")
-    done
-  fi
+  local dev
+  dev="$(df -P "$BASE_DIR" | awk 'NR==2 {print $1}')"
 
-  if [[ "${#ORDER[@]}" -eq 0 ]]; then
-    echo "ORDER пустой. Укажите частоты в ORDER или задайте CAPTURE_ORDER." >&2
+  if [[ "$dev" != ${EXPECTED_DEVICE_PREFIX}* ]]; then
+    echo "BASE_DIR=$BASE_DIR сейчас находится на $dev, а ожидался ${EXPECTED_DEVICE_PREFIX}*" >&2
+    echo "Исправь BASE_DIR на точку монтирования nvme1n1" >&2
     exit 1
   fi
-
-  log "Рабочая директория: $SCRIPT_DIR"
-  log "BASE_DIR: $BASE_DIR"
-  log "ORDER: ${ORDER[*]}"
-  log "RUN_ONCE: $RUN_ONCE"
-
 }
 
 run_one_freq() {
@@ -225,23 +118,14 @@ run_one_freq() {
   local serial="${SERIAL[$band]}"
   local freq="${FREQ_HZ[$band]}"
 
-  if [[ -z "$serial" ]]; then
-    log "Для band=$band пустой serial, пропускаю"
-    return 0
-  fi
-
   local ts out_dir log_file
   ts="$(date '+%F_%H-%M-%S')"
   out_dir="$BASE_DIR/$band/$ts"
   log_file="$out_dir/run.log"
 
-  log "Старт band=$band serial=$serial freq=$freq dir=$out_dir"
-  if ! mkdir -p "$out_dir"; then
-    log "Не удалось создать каталог $out_dir"
-    return 1
-  fi
+  mkdir -p "$out_dir"
 
-  stop_band_service "$band"
+  log "Старт band=$band serial=$serial freq=$freq dir=$out_dir"
 
   "$PYTHON_BIN" "$SCRIPT_PATH" \
     --serial "$serial" \
@@ -257,7 +141,6 @@ run_one_freq() {
     >"$log_file" 2>&1 &
 
   local pid=$!
-  CURRENT_CAPTURE_PID="$pid"
   log "PID=$pid"
 
   while kill -0 "$pid" 2>/dev/null; do
@@ -266,11 +149,9 @@ run_one_freq() {
     cur_total_size="$(total_size_bytes)"
 
     if (( cur_total_size >= TOTAL_LIMIT_BYTES )); then
-      log "Достигнут общий лимит $lim_all GiB. Останавливаю PID=$pid"
+      log "Достигнут общий лимит 512 GiB. Останавливаю PID=$pid"
       kill -TERM "$pid" 2>/dev/null || true
       wait "$pid" 2>/dev/null || true
-      CURRENT_CAPTURE_PID=""
-      start_current_service
       return 2
     fi
 
@@ -297,8 +178,6 @@ run_one_freq() {
     sleep 1
   done
 
-  CURRENT_CAPTURE_PID=""
-  start_current_service
   log "Завершен band=$band, размер=$(du -sh "$out_dir" | awk '{print $1}')"
   return 0
 }
@@ -309,7 +188,7 @@ main_loop() {
     total_before="$(total_size_bytes)"
 
     if (( total_before >= TOTAL_LIMIT_BYTES )); then
-      log "Общий размер уже >=  GiB, выхожу"
+      log "Общий размер уже >= 512 GiB, выхожу"
       break
     fi
 
@@ -327,18 +206,10 @@ main_loop() {
         if [[ $rc -eq 2 ]]; then
           log "Остановка по общему лимиту"
           return 0
-        else
-          log "Ошибка записи band=$band rc=$rc"
-          return "$rc"
         fi
       }
     done
 
-    if [[ "$RUN_ONCE" == "1" ]]; then
-      log "RUN_ONCE=1, завершаю работу после одного цикла"
-      break
-    fi
-
     elapsed=$(( $(date +%s) - cycle_start ))
     sleep_left=$(( CYCLE_SECONDS - elapsed ))
 
@@ -351,9 +222,5 @@ main_loop() {
   done
 }
 
-trap cleanup_capture EXIT
-trap 'on_signal INT' INT
-trap 'on_signal TERM' TERM
-
 ensure_requirements
-main_loop
+main_loop

deploy/docker/docker-compose.yml

@@ -11,18 +11,7 @@ services:
       - PYTHONPATH=/app
       - JAMMER_STATE_FILE=/app/runtime/jammer_active.flag
     working_dir: /app
-    command:
-      - uvicorn
-      - src.server_to_master:app
-      - --host
-      - 0.0.0.0
-      - --port
-      - "5010"
-      - --reload
-      - --reload-dir
-      - /app/src
-      - --reload-dir
-      - /app/common
+    command: ["python3", "-m", "src.server_to_master"]
     restart: unless-stopped
     ports:
       - "5010:5010"
@@ -78,18 +67,7 @@ services:
     environment:
       - PYTHONPATH=/app
     working_dir: /app
-    command:
-      - uvicorn
-      - telemetry.telemetry_server:app
-      - --host
-      - 0.0.0.0
-      - --port
-      - "5020"
-      - --reload
-      - --reload-dir
-      - /app/telemetry
-      - --reload-dir
-      - /app/common
+    command: ["python3", "-m", "telemetry.telemetry_server"]
     restart: unless-stopped
     ports:
       - "5020:5020"

deploy/systemd/dronedetector-compose.service

@@ -8,9 +8,9 @@ Requires=docker.service
 Type=oneshot
 WorkingDirectory=__PROJECT_ROOT__
 RemainAfterExit=yes
-ExecStart=/usr/bin/docker compose -f __PROJECT_ROOT__/deploy/docker/docker-compose.yml up -d
+ExecStart=/usr/bin/docker compose -f __PROJECT_ROOT__/deploy/docker/docker-compose.yml up -d --build
 ExecStop=/usr/bin/docker compose -f __PROJECT_ROOT__/deploy/docker/docker-compose.yml down
-ExecReload=/usr/bin/docker compose -f __PROJECT_ROOT__/deploy/docker/docker-compose.yml up -d
+ExecReload=/usr/bin/docker compose -f __PROJECT_ROOT__/deploy/docker/docker-compose.yml up -d --build
 TimeoutStartSec=0
 
 [Install]

deploy/systemd/dronedetector-sdr-1200.service

@@ -10,7 +10,8 @@ Group=__RUN_GROUP__
 WorkingDirectory=__PROJECT_ROOT__
 EnvironmentFile=__PROJECT_ROOT__/.env
 Environment=PYTHONPATH=__PROJECT_ROOT__
-ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python src/main_1200.py
+ExecStartPre=/usr/local/bin/dronedetector-precheck-sdr.sh
+ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python orange_scripts/main_1200.py
 Restart=always
 RestartSec=3
 

deploy/systemd/dronedetector-sdr-1500.service

@@ -10,6 +10,7 @@ Group=__RUN_GROUP__
 WorkingDirectory=__PROJECT_ROOT__
 EnvironmentFile=__PROJECT_ROOT__/.env
 Environment=PYTHONPATH=__PROJECT_ROOT__
+ExecStartPre=/usr/local/bin/dronedetector-precheck-sdr.sh
 ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python src/main_1500.py
 Restart=always
 RestartSec=3

deploy/systemd/dronedetector-sdr-2400.service

@@ -10,7 +10,8 @@ Group=__RUN_GROUP__
 WorkingDirectory=__PROJECT_ROOT__
 EnvironmentFile=__PROJECT_ROOT__/.env
 Environment=PYTHONPATH=__PROJECT_ROOT__
-ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python src/main_2400.py
+ExecStartPre=/usr/local/bin/dronedetector-precheck-sdr.sh
+ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python orange_scripts/main_2400.py
 Restart=always
 RestartSec=3
 

deploy/systemd/dronedetector-sdr-3300.service

@@ -10,6 +10,7 @@ Group=__RUN_GROUP__
 WorkingDirectory=__PROJECT_ROOT__
 EnvironmentFile=__PROJECT_ROOT__/.env
 Environment=PYTHONPATH=__PROJECT_ROOT__
+ExecStartPre=/usr/local/bin/dronedetector-precheck-sdr.sh
 ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python src/main_3300.py
 Restart=always
 RestartSec=3

deploy/systemd/dronedetector-sdr-433.service

@@ -10,6 +10,7 @@ Group=__RUN_GROUP__
 WorkingDirectory=__PROJECT_ROOT__
 EnvironmentFile=__PROJECT_ROOT__/.env
 Environment=PYTHONPATH=__PROJECT_ROOT__
+ExecStartPre=/usr/local/bin/dronedetector-precheck-sdr.sh
 ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python src/main_433.py
 Restart=always
 RestartSec=3

deploy/systemd/dronedetector-sdr-4500.service

@@ -10,6 +10,7 @@ Group=__RUN_GROUP__
 WorkingDirectory=__PROJECT_ROOT__
 EnvironmentFile=__PROJECT_ROOT__/.env
 Environment=PYTHONPATH=__PROJECT_ROOT__
+ExecStartPre=/usr/local/bin/dronedetector-precheck-sdr.sh
 ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python src/main_4500.py
 Restart=always
 RestartSec=3

deploy/systemd/dronedetector-sdr-5200.service

@@ -10,6 +10,7 @@ Group=__RUN_GROUP__
 WorkingDirectory=__PROJECT_ROOT__
 EnvironmentFile=__PROJECT_ROOT__/.env
 Environment=PYTHONPATH=__PROJECT_ROOT__
+ExecStartPre=/usr/local/bin/dronedetector-precheck-sdr.sh
 ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python src/main_5200.py
 Restart=always
 RestartSec=3

deploy/systemd/dronedetector-sdr-5800.service

@@ -10,6 +10,7 @@ Group=__RUN_GROUP__
 WorkingDirectory=__PROJECT_ROOT__
 EnvironmentFile=__PROJECT_ROOT__/.env
 Environment=PYTHONPATH=__PROJECT_ROOT__
+ExecStartPre=/usr/local/bin/dronedetector-precheck-sdr.sh
 ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python src/main_5800.py
 Restart=always
 RestartSec=3

deploy/systemd/dronedetector-sdr-750.service

@@ -10,6 +10,7 @@ Group=__RUN_GROUP__
 WorkingDirectory=__PROJECT_ROOT__
 EnvironmentFile=__PROJECT_ROOT__/.env
 Environment=PYTHONPATH=__PROJECT_ROOT__
+ExecStartPre=/usr/local/bin/dronedetector-precheck-sdr.sh
 ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python src/main_750.py
 Restart=always
 RestartSec=3

deploy/systemd/dronedetector-sdr-868-915.service

@@ -0,0 +1,19 @@
+[Unit]
+Description=DroneDetector SDR Router 868/915 Shared HackRF
+After=network-online.target
+Wants=network-online.target
+
+[Service]
+Type=simple
+User=__RUN_USER__
+Group=__RUN_GROUP__
+WorkingDirectory=__PROJECT_ROOT__
+EnvironmentFile=__PROJECT_ROOT__/.env
+Environment=PYTHONPATH=__PROJECT_ROOT__
+ExecStartPre=/usr/local/bin/dronedetector-precheck-sdr.sh
+ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python src/main_868_915_router.py
+Restart=always
+RestartSec=3
+
+[Install]
+WantedBy=multi-user.target

deploy/systemd/dronedetector-sdr-868.service

@@ -0,0 +1,20 @@
+[Unit]
+Description=DroneDetector SDR Scanner 868 MHz
+After=network-online.target dronedetector-sdr-868-915.service
+Wants=network-online.target
+Requires=dronedetector-sdr-868-915.service
+
+[Service]
+Type=simple
+User=__RUN_USER__
+Group=__RUN_GROUP__
+WorkingDirectory=__PROJECT_ROOT__
+EnvironmentFile=__PROJECT_ROOT__/.env
+Environment=PYTHONPATH=__PROJECT_ROOT__
+ExecStartPre=/usr/local/bin/dronedetector-precheck-sdr.sh
+ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python src/main_868.py
+Restart=always
+RestartSec=3
+
+[Install]
+WantedBy=multi-user.target

deploy/systemd/dronedetector-sdr-915.service

@@ -1,7 +1,8 @@
 [Unit]
 Description=DroneDetector SDR Scanner 915 MHz
-After=network-online.target
+After=network-online.target dronedetector-sdr-868-915.service
 Wants=network-online.target
+Requires=dronedetector-sdr-868-915.service
 
 [Service]
 Type=simple
@@ -10,7 +11,8 @@ Group=__RUN_GROUP__
 WorkingDirectory=__PROJECT_ROOT__
 EnvironmentFile=__PROJECT_ROOT__/.env
 Environment=PYTHONPATH=__PROJECT_ROOT__
-ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python src/main_915.py
+ExecStartPre=/usr/local/bin/dronedetector-precheck-sdr.sh
+ExecStart=__PROJECT_ROOT__/.venv-sdr/bin/python orange_scripts/main_915.py
 Restart=always
 RestartSec=3
 

deploy/systemd/precheck-sdr.sh

@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+if ! command -v hackrf_info >/dev/null 2>&1; then
+  echo "[dronedetector-precheck] hackrf_info not found. Install hackrf-tools/hackrf package." >&2
+  exit 1
+fi
+
+if ! command -v gnuradio-config-info >/dev/null 2>&1; then
+  echo "[dronedetector-precheck] gnuradio-config-info not found. Install gnuradio." >&2
+  exit 1
+fi
+
+if ! python3 -c "import osmosdr" >/dev/null 2>&1; then
+  echo "[dronedetector-precheck] Python module osmosdr not importable." >&2
+  exit 1
+fi
+
+if ! hackrf_info 2>/dev/null | grep -q "Found HackRF"; then
+  echo "[dronedetector-precheck] HackRF device was not detected by hackrf_info." >&2
+  exit 1
+fi

install_all.sh

@@ -7,13 +7,11 @@ SYSTEMD_TARGET_DIR="/etc/systemd/system"
 RUN_USER="${SUDO_USER:-${USER}}"
 RUN_GROUP="$(id -gn "${RUN_USER}")"
 
-SOURCE_ARCHIVES=(
-  "torchsig.tar.gz:torchsig:pyproject.toml"
-)
-
 SDR_UNITS=(
   dronedetector-sdr-433.service
   dronedetector-sdr-750.service
+  dronedetector-sdr-868-915.service
+  dronedetector-sdr-868.service
   dronedetector-sdr-1500.service
   dronedetector-sdr-3300.service
   dronedetector-sdr-4500.service
@@ -24,8 +22,6 @@ SDR_UNITS=(
   dronedetector-sdr-2400.service
 )
 
-CONFIGURED_SDR_UNITS=()
-
 log() {
   printf '[install_all] %s\n' "$*"
 }
@@ -35,51 +31,6 @@ die() {
   exit 1
 }
 
-sdr_unit_env_key() {
-  local unit="$1"
-  local band="${unit#dronedetector-sdr-}"
-  band="${band%.service}"
-  printf 'hack_%s\n' "$band"
-}
-
-get_env_value() {
-  local key="$1"
-  awk -F= -v key="$key" '
-    $1 == key {
-      value = substr($0, index($0, "=") + 1)
-      sub(/^[[:space:]]+/, "", value)
-      sub(/[[:space:]]+$/, "", value)
-      gsub(/^"/, "", value)
-      gsub(/"$/, "", value)
-      gsub(/^'\''/, "", value)
-      gsub(/'\''$/, "", value)
-      print value
-      exit
-    }
-  ' "${PROJECT_ROOT}/.env"
-}
-
-populate_configured_sdr_units() {
-  CONFIGURED_SDR_UNITS=()
-
-  local unit env_key env_value
-  for unit in "${SDR_UNITS[@]}"; do
-    env_key="$(sdr_unit_env_key "$unit")"
-    env_value="$(get_env_value "$env_key")"
-    if [[ -n "$env_value" ]]; then
-      CONFIGURED_SDR_UNITS+=("$unit")
-    else
-      log "Skipping ${unit}: ${env_key} is empty in .env"
-    fi
-  done
-
-  if [[ "${#CONFIGURED_SDR_UNITS[@]}" -eq 0 ]]; then
-    log "No SDR units are configured in .env"
-  else
-    log "Configured SDR units: ${CONFIGURED_SDR_UNITS[*]}"
-  fi
-}
-
 print_failure_logs() {
   log "Collecting diagnostics..."
   systemctl --no-pager --full status dronedetector-compose.service || true
@@ -108,35 +59,6 @@ require_root() {
   fi
 }
 
-extract_local_source_archives() {
-  local spec archive_rel target_rel sentinel_rel
-  local archive_path target_path sentinel_path
-
-  for spec in "${SOURCE_ARCHIVES[@]}"; do
-    IFS=':' read -r archive_rel target_rel sentinel_rel <<< "$spec"
-    archive_path="${PROJECT_ROOT}/${archive_rel}"
-    target_path="${PROJECT_ROOT}/${target_rel}"
-    sentinel_path="${target_path}/${sentinel_rel}"
-
-    if [[ -f "${sentinel_path}" ]]; then
-      log "Vendored source already available: ${target_rel}"
-      continue
-    fi
-
-    if [[ -e "${target_path}" ]]; then
-      die "Found ${target_path}, but ${sentinel_rel} is missing. Remove or repair this directory, then rerun the installer."
-    fi
-
-    [[ -f "${archive_path}" ]] || die "Missing vendored source ${target_path} and archive ${archive_path}"
-    command -v tar >/dev/null 2>&1 || die "tar is required to unpack ${archive_rel}"
-
-    log "Extracting ${archive_rel} -> ${target_rel}"
-    tar -xzf "${archive_path}" -C "${PROJECT_ROOT}"
-    [[ -f "${sentinel_path}" ]] || die "Archive ${archive_path} did not unpack expected file ${sentinel_path}"
-    chown -R "${RUN_USER}:${RUN_GROUP}" "${target_path}"
-  done
-}
-
 preflight() {
   log "Preflight checks"
   [[ -f "${PROJECT_ROOT}/.env" ]] || die "Missing ${PROJECT_ROOT}/.env"
@@ -249,6 +171,8 @@ build_and_run_compose() {
 install_systemd_units() {
   log "Installing systemd units"
 
+  install -m 0755 "${PROJECT_ROOT}/deploy/systemd/precheck-sdr.sh" /usr/local/bin/dronedetector-precheck-sdr.sh
+
   local src dst
   for src in "${PROJECT_ROOT}"/deploy/systemd/*.service; do
     dst="${SYSTEMD_TARGET_DIR}/$(basename "$src")"
@@ -263,7 +187,7 @@ install_systemd_units() {
   systemctl enable dronedetector-compose.service
   systemctl restart dronedetector-compose.service
 
-  for unit in "${CONFIGURED_SDR_UNITS[@]}"; do
+  for unit in "${SDR_UNITS[@]}"; do
     systemctl enable "$unit"
     systemctl restart "$unit"
   done
@@ -288,7 +212,7 @@ verify_installation() {
   log "Verifying services"
 
   wait_for_systemd_active dronedetector-compose.service 30 || die "dronedetector-compose.service is not active"
-  for unit in "${CONFIGURED_SDR_UNITS[@]}"; do
+  for unit in "${SDR_UNITS[@]}"; do
     wait_for_systemd_active "$unit" 45 || die "$unit is not active"
   done
 
@@ -298,7 +222,7 @@ verify_installation() {
   running_services="$(docker compose -f "$COMPOSE_FILE" ps --status running --services || true)"
 
   printf '%s\n' "$running_services" | grep -Fxq "dronedetector-server-to-master" || die "server_to_master is not running"
-  printf '%s\n' "$running_services" | grep -Fxq "dronedetector-telemetry-server" || die "telemetry_server is not running"
+  printf '%s\n' "$running_services" | grep -Fxq "dronedetector-nn-server" || die "NN_server is not running"
 
   log "Verification completed"
 }
@@ -309,9 +233,7 @@ main() {
   log "Project root: ${PROJECT_ROOT}"
   log "Runtime user: ${RUN_USER}:${RUN_GROUP}"
 
-  extract_local_source_archives
   preflight
-  populate_configured_sdr_units
   install_host_non_python_deps
   setup_sdr_python_env
   install_docker_if_needed

install_all_train.sh

@@ -28,7 +28,7 @@ run_as_root() {
 
 preflight() {
   [[ -f "${PROJECT_ROOT}/deploy/requirements/nn_gpu_pinned.txt" ]] || die "Missing deploy/requirements/nn_gpu_pinned.txt"
-  [[ -f "${PROJECT_ROOT}/requirements-train.txt" ]] || die "Missing train_scripts/requirements-train.txt"
+  [[ -f "${PROJECT_ROOT}/train_scripts/requirements-train.txt" ]] || die "Missing train_scripts/requirements-train.txt"
   [[ -f "${PROJECT_ROOT}/torchsig/pyproject.toml" ]] || die "Missing local torchsig package"
   command -v "${PYTHON_BIN}" >/dev/null 2>&1 || die "${PYTHON_BIN} not found"
 }

orange_scripts/compose_send_data_2400.py

@@ -1,115 +1,115 @@
-
-from common.runtime import load_root_env, validate_env, as_float, as_int, as_str
-import numpy as np
-import requests
-import os
-import sys
-import json
-import time
-
-
+
+from common.runtime import load_root_env, validate_env, as_float, as_int, as_str
+import numpy as np
+import requests
+import os
+import sys
+import json
+import time
+
+
 load_root_env(__file__)
 validate_env("orange_scripts/compose_send_data_2400.py", {
     "POROG_2400": as_float,
     "SERVER_IP_2": as_str,
     "SERVER_PORT_2": as_int,
 })
-porog = float(os.getenv('POROG_2400'))
-server_ip_1 = os.getenv('SERVER_IP_1')
-server_port_1 = os.getenv('SERVER_PORT_1')
-server_ip_2 = os.getenv('SERVER_IP_2')
-server_port_2 = os.getenv('SERVER_PORT_2')
-PARAMS = {'split_size': 400_000, 'point_amount': 100_000}
-PARAMS['show_amount'] = 0.8 * PARAMS['point_amount']
-token = 0
-channel = 1
-flag = 0
-
-##############################
-# HYPERPARAMETERS
-##############################
-f_base = 2.4e9
-f_step = 20e6
-f_roof = 2.5e9
-##############################
-# Variables
-##############################
-f = f_base
-EOCF = 0
-signal_arr = []
-
-
-class NumpyArrayEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, np.integer):
-            return int(obj)
-        elif isinstance(obj, np.ndarray):
-            return obj.tolist()
-        else:
-            return super(NumpyArrayEncoder, self).default(obj)
-
-
-def send_data(sig):
-    try:
-        global token
-        print('#' * 10)
-        print('\nОтправка пакета ' + str(token+1))
-        data_to_send = {
-            "freq": 2400,
-            "channel": int(channel),
-            "token": int(token+1),
-            "data_real": np.asarray(np.array(sig, dtype=np.complex64).real, dtype=np.float32),
-            "data_imag": np.asarray(np.array(sig, dtype=np.complex64).imag, dtype=np.float32)
-        }
-        mod_data_to_send = json.dumps(data_to_send, cls=NumpyArrayEncoder)
-        response = requests.post("http://{0}:{1}/receive_data".format(server_ip_2, server_port_2), json=mod_data_to_send)
-        if response.status_code == 200:
-            token += 1
-            print(response.text)
-            print('#' * 10)       
-        else:
-            print("Ошибка при отправке данных: ", response.status_code)
-            print('#' * 10)
-    except Exception as exc:
-        print(str(exc))
-
-
-def median(sig):
-    global flag
-    median = abs(float(np.median(sorted(np.asarray(np.abs(np.array(sig, dtype=np.complex64)), dtype=np.float32))[int(PARAMS['show_amount']):])))
-    flag = 0 if porog > median else 1
-    print(channel, median, flag)
-
-
-def work(lvl):
-    global flag
-    global channel
-    global f_base
-    global f_step
-    global f_roof
-    global f
-    global EOCF
-    global signal_arr
-
-    y = np.array(lvl).ravel()
-    signal_arr = np.concatenate((signal_arr, y), axis=None)
-
-    if f >= f_roof:
-        f = f_base
-        signal_arr = []
-        channel = 1
-        return f, EOCF
-    else:
-        if flag == 0 and len(signal_arr) >= PARAMS['point_amount']:
-            median(signal_arr[:PARAMS['point_amount']])
-            signal_arr = []
-            if flag == 0:
-                f += f_step
-                channel += 1
-        if len(signal_arr) >= PARAMS['split_size']:
-            send_data(signal_arr[:PARAMS['split_size']])
-            flag = 0
-            signal_arr = []
-            channel += 1
-            f += f_step
-        return f, EOCF
+porog = float(os.getenv('POROG_2400'))
+server_ip_1 = os.getenv('SERVER_IP_1')
+server_port_1 = os.getenv('SERVER_PORT_1')
+server_ip_2 = os.getenv('SERVER_IP_2')
+server_port_2 = os.getenv('SERVER_PORT_2')
+PARAMS = {'split_size': 400_000, 'point_amount': 100_000}
+PARAMS['show_amount'] = 0.8 * PARAMS['point_amount']
+token = 0
+channel = 1
+flag = 0
+
+##############################
+# HYPERPARAMETERS
+##############################
+f_base = 2.4e9
+f_step = 20e6
+f_roof = 2.5e9
+##############################
+# Variables
+##############################
+f = f_base
+EOCF = 0
+signal_arr = []
+
+
+class NumpyArrayEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        else:
+            return super(NumpyArrayEncoder, self).default(obj)
+
+
+def send_data(sig):
+    try:
+        global token
+        print('#' * 10)
+        print('\nОтправка пакета ' + str(token+1))
+        data_to_send = {
+            "freq": 2400,
+            "channel": int(channel),
+            "token": int(token+1),
+            "data_real": np.asarray(np.array(sig, dtype=np.complex64).real, dtype=np.float32),
+            "data_imag": np.asarray(np.array(sig, dtype=np.complex64).imag, dtype=np.float32)
+        }
+        mod_data_to_send = json.dumps(data_to_send, cls=NumpyArrayEncoder)
+        response = requests.post("http://{0}:{1}/receive_data".format(server_ip_2, server_port_2), json=mod_data_to_send)
+        if response.status_code == 200:
+            token += 1
+            print(response.text)
+            print('#' * 10)       
+        else:
+            print("Ошибка при отправке данных: ", response.status_code)
+            print('#' * 10)
+    except Exception as exc:
+        print(str(exc))
+
+
+def median(sig):
+    global flag
+    median = abs(float(np.median(sorted(np.asarray(np.abs(np.array(sig, dtype=np.complex64)), dtype=np.float32))[int(PARAMS['show_amount']):])))
+    flag = 0 if porog > median else 1
+    print(channel, median, flag)
+
+
+def work(lvl):
+    global flag
+    global channel
+    global f_base
+    global f_step
+    global f_roof
+    global f
+    global EOCF
+    global signal_arr
+
+    y = np.array(lvl).ravel()
+    signal_arr = np.concatenate((signal_arr, y), axis=None)
+
+    if f >= f_roof:
+        f = f_base
+        signal_arr = []
+        channel = 1
+        return f, EOCF
+    else:
+        if flag == 0 and len(signal_arr) >= PARAMS['point_amount']:
+            median(signal_arr[:PARAMS['point_amount']])
+            signal_arr = []
+            if flag == 0:
+                f += f_step
+                channel += 1
+        if len(signal_arr) >= PARAMS['split_size']:
+            send_data(signal_arr[:PARAMS['split_size']])
+            flag = 0
+            signal_arr = []
+            channel += 1
+            f += f_step
+        return f, EOCF

orange_scripts/compose_send_data_5800.py

@@ -1,115 +0,0 @@
-
-from common.runtime import load_root_env, validate_env, as_float, as_int, as_str
-import numpy as np
-import requests
-import os
-import sys
-import json
-import time
-
-
-load_root_env(__file__)
-validate_env("orange_scripts/compose_send_data_5800.py", {
-    "POROG_5800": as_float,
-    "SERVER_IP_2": as_str,
-    "SERVER_PORT_2": as_int,
-})
-porog = float(os.getenv('POROG_5800'))
-server_ip_1 = os.getenv('SERVER_IP_1')
-server_port_1 = os.getenv('SERVER_PORT_1')
-server_ip_2 = os.getenv('SERVER_IP_2')
-server_port_2 = os.getenv('SERVER_PORT_2')
-PARAMS = {'split_size': 400_000, 'point_amount': 100_000}
-PARAMS['show_amount'] = 0.8 * PARAMS['point_amount']
-token = 0
-channel = 1
-flag = 0
-
-##############################
-# HYPERPARAMETERS
-##############################
-f_base = 5.9e9
-f_step = 20e6
-f_roof = 5.7e9
-##############################
-# Variables
-##############################
-f = f_base
-EOCF = 0
-signal_arr = []
-
-
-class NumpyArrayEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, np.integer):
-            return int(obj)
-        elif isinstance(obj, np.ndarray):
-            return obj.tolist()
-        else:
-            return super(NumpyArrayEncoder, self).default(obj)
-
-
-def send_data(sig):
-    try:
-        global token
-        print('#' * 10)
-        print('\nОтправка пакета ' + str(token+1))
-        data_to_send = {
-            "freq": 5800,
-            "channel": int(channel),
-            "token": int(token+1),
-            "data_real": np.asarray(np.array(sig, dtype=np.complex64).real, dtype=np.float32),
-            "data_imag": np.asarray(np.array(sig, dtype=np.complex64).imag, dtype=np.float32)
-        }
-        mod_data_to_send = json.dumps(data_to_send, cls=NumpyArrayEncoder)
-        response = requests.post("http://{0}:{1}/receive_data".format(server_ip_2, server_port_2), json=mod_data_to_send)
-        if response.status_code == 200:
-            token += 1
-            print(response.text)
-            print('#' * 10)       
-        else:
-            print("Ошибка при отправке данных: ", response.status_code)
-            print('#' * 10)
-    except Exception as exc:
-        print(str(exc))
-
-
-def median(sig):
-    global flag
-    median = abs(float(np.median(sorted(np.asarray(np.abs(np.array(sig, dtype=np.complex64)), dtype=np.float32))[int(PARAMS['show_amount']):])))
-    flag = 0 if porog > median else 1
-    print(channel, median, flag)
-
-
-def work(lvl):
-    global flag
-    global channel
-    global f_base
-    global f_step
-    global f_roof
-    global f
-    global EOCF
-    global signal_arr
-
-    y = np.array(lvl).ravel()
-    signal_arr = np.concatenate((signal_arr, y), axis=None)
-
-    if f >= f_roof:
-        f = f_base
-        signal_arr = []
-        channel = 1
-        return f, EOCF
-    else:
-        if flag == 0 and len(signal_arr) >= PARAMS['point_amount']:
-            median(signal_arr[:PARAMS['point_amount']])
-            signal_arr = []
-            if flag == 0:
-                f += f_step
-                channel += 1
-        if len(signal_arr) >= PARAMS['split_size']:
-            send_data(signal_arr[:PARAMS['split_size']])
-            flag = 0
-            signal_arr = []
-            channel += 1
-            f += f_step
-        return f, EOCF

orange_scripts/main_2400.py

@@ -1,172 +1,172 @@
-from gnuradio import blocks, gr
-import sys
-import signal
-import compose_send_data_2400 as my_freq
-import osmosdr
-import time
-import threading
-import subprocess
-import os
-from common.runtime import load_root_env, resolve_hackrf_index
-
-
-load_root_env(__file__)
-
-def get_hack_id():
-    return resolve_hackrf_index('HACKID_2400', 'orange_scripts/main_2400.py')
-    serial_number = os.getenv('HACKID_2400')
-    pos = None
-    output = []
-    try:
-        command = 'lsusb -v -d 1d50:6089 | grep iSerial'
-        output.append(subprocess.check_output(command, shell=True, text=True))
-    except subprocess.CalledProcessError as e:
-        print(f"Команда завершилась с кодом возврата {e.returncode}")
-        print(e)
-    print(output)
-    output_lines = output[0].strip().split('\n')
-    print(output_lines)
-    serial_numbers = [line.split()[-1] for line in output_lines]
-    print(serial_numbers)
-    for i, number in enumerate(serial_numbers):
-        if number == serial_number:
-            id = i
-            break
-    if id is not None:
-        print('HackId is: {0}'.format(id))
-        return str(id)
-    else:
-        print('Такого хака нет!')
-
-
-class get_center_freq(gr.top_block):
-
-    def __init__(self):
-        gr.top_block.__init__(self, "get_center_freq")
-
-        ##################################################
-        # Variables
-        ##################################################
-        self.prob_freq = prob_freq = 0
-        self.top_peaks_amount = top_peaks_amount = 20
-        self.samp_rate = samp_rate = 20e6
-        self.poll_rate = poll_rate = 10000
-        self.num_points = num_points = 8192
-        self.flag = flag = 1
-        self.decimation = decimation = 1
-        self.center_freq = center_freq = my_freq.work(prob_freq)[0]
-
-        ##################################################
-        # Blocks
-        ##################################################
-        self.probSigVec = blocks.probe_signal_vc(4096)
-        self.rtlsdr_source_0 = osmosdr.source(
-            args="numchan=" + str(1) + " " + 'hackrf=' + get_hack_id()
-        )
-        self.rtlsdr_source_0.set_time_unknown_pps(osmosdr.time_spec_t())
-        self.rtlsdr_source_0.set_sample_rate(samp_rate)
-        self.rtlsdr_source_0.set_center_freq(center_freq, 0)
-        self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(16, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 0)
-        self.rtlsdr_source_0.set_bb_gain(0, 0)
-        self.rtlsdr_source_0.set_antenna('', 0)
-        self.rtlsdr_source_0.set_bandwidth(0, 0)
-        self.rtlsdr_source_0.set_min_output_buffer(4096)
-
-        def _prob_freq_probe():
-            while True:
-
-                val = self.probSigVec.level()
-                try:
-                    self.set_prob_freq(val)
-                except AttributeError:
-                    pass
-                time.sleep(1.0 / (poll_rate))
-        _prob_freq_thread = threading.Thread(target=_prob_freq_probe)
-        _prob_freq_thread.daemon = True
-        _prob_freq_thread.start()
-
-        self.blocks_stream_to_vector_1 = blocks.stream_to_vector(gr.sizeof_gr_complex*1, 4096)
-
-
-
-        ##################################################
-        # Connections
-        ##################################################
-        self.connect((self.blocks_stream_to_vector_1, 0), (self.probSigVec, 0))
-        self.connect((self.rtlsdr_source_0, 0), (self.blocks_stream_to_vector_1, 0))
-
-    def get_prob_freq(self):
-        return self.prob_freq
-
-    def set_prob_freq(self, prob_freq):
-        self.prob_freq = prob_freq
-        self.set_center_freq(my_freq.work(self.prob_freq)[0])
-
-    def get_top_peaks_amount(self):
-        return self.top_peaks_amount
-
-    def set_top_peaks_amount(self, top_peaks_amount):
-        self.top_peaks_amount = top_peaks_amount
-
-    def get_samp_rate(self):
-        return self.samp_rate
-
-    def set_samp_rate(self, samp_rate):
-        self.samp_rate = samp_rate
-        self.rtlsdr_source_0.set_sample_rate(self.samp_rate)
-
-    def get_poll_rate(self):
-        return self.poll_rate
-
-    def set_poll_rate(self, poll_rate):
-        self.poll_rate = poll_rate
-
-    def get_num_points(self):
-        return self.num_points
-
-    def set_num_points(self, num_points):
-        self.num_points = num_points
-
-    def get_flag(self):
-        return self.flag
-
-    def set_flag(self, flag):
-        self.flag = flag
-
-    def get_decimation(self):
-        return self.decimation
-
-    def set_decimation(self, decimation):
-        self.decimation = decimation
-
-    def get_center_freq(self):
-        return self.center_freq
-
-    def set_center_freq(self, center_freq):
-        self.center_freq = center_freq
-        self.rtlsdr_source_0.set_center_freq(self.center_freq, 0)
-
-
-def main(top_block_cls=get_center_freq, options=None):
-    time.sleep(3)
-    tb = top_block_cls()
-    def sig_handler(sig=None, frame=None):
-        tb.stop()
-        tb.wait()
-        sys.exit(0)
-
-    signal.signal(signal.SIGINT, sig_handler)
-    signal.signal(signal.SIGTERM, sig_handler)
-
-    tb.start()
-    try:
-        input('Press Enter to quit: ')
-    except EOFError:
-        pass
-    tb.wait()
-
-
-if __name__ == '__main__':
-    main()
+from gnuradio import blocks, gr
+import sys
+import signal
+import compose_send_data_2400 as my_freq
+import osmosdr
+import time
+import threading
+import subprocess
+import os
+from common.runtime import load_root_env, resolve_hackrf_index
+
+
+load_root_env(__file__)
+
+def get_hack_id():
+    return resolve_hackrf_index('HACKID_2400', 'orange_scripts/main_2400.py')
+    serial_number = os.getenv('HACKID_2400')
+    pos = None
+    output = []
+    try:
+        command = 'lsusb -v -d 1d50:6089 | grep iSerial'
+        output.append(subprocess.check_output(command, shell=True, text=True))
+    except subprocess.CalledProcessError as e:
+        print(f"Команда завершилась с кодом возврата {e.returncode}")
+        print(e)
+    print(output)
+    output_lines = output[0].strip().split('\n')
+    print(output_lines)
+    serial_numbers = [line.split()[-1] for line in output_lines]
+    print(serial_numbers)
+    for i, number in enumerate(serial_numbers):
+        if number == serial_number:
+            id = i
+            break
+    if id is not None:
+        print('HackId is: {0}'.format(id))
+        return str(id)
+    else:
+        print('Такого хака нет!')
+
+
+class get_center_freq(gr.top_block):
+
+    def __init__(self):
+        gr.top_block.__init__(self, "get_center_freq")
+
+        ##################################################
+        # Variables
+        ##################################################
+        self.prob_freq = prob_freq = 0
+        self.top_peaks_amount = top_peaks_amount = 20
+        self.samp_rate = samp_rate = 20e6
+        self.poll_rate = poll_rate = 10000
+        self.num_points = num_points = 8192
+        self.flag = flag = 1
+        self.decimation = decimation = 1
+        self.center_freq = center_freq = my_freq.work(prob_freq)[0]
+
+        ##################################################
+        # Blocks
+        ##################################################
+        self.probSigVec = blocks.probe_signal_vc(4096)
+        self.rtlsdr_source_0 = osmosdr.source(
+            args="numchan=" + str(1) + " " + 'hackrf=' + get_hack_id()
+        )
+        self.rtlsdr_source_0.set_time_unknown_pps(osmosdr.time_spec_t())
+        self.rtlsdr_source_0.set_sample_rate(samp_rate)
+        self.rtlsdr_source_0.set_center_freq(center_freq, 0)
+        self.rtlsdr_source_0.set_freq_corr(0, 0)
+        self.rtlsdr_source_0.set_gain(16, 0)
+        self.rtlsdr_source_0.set_if_gain(16, 0)
+        self.rtlsdr_source_0.set_bb_gain(0, 0)
+        self.rtlsdr_source_0.set_antenna('', 0)
+        self.rtlsdr_source_0.set_bandwidth(0, 0)
+        self.rtlsdr_source_0.set_min_output_buffer(4096)
+
+        def _prob_freq_probe():
+            while True:
+
+                val = self.probSigVec.level()
+                try:
+                    self.set_prob_freq(val)
+                except AttributeError:
+                    pass
+                time.sleep(1.0 / (poll_rate))
+        _prob_freq_thread = threading.Thread(target=_prob_freq_probe)
+        _prob_freq_thread.daemon = True
+        _prob_freq_thread.start()
+
+        self.blocks_stream_to_vector_1 = blocks.stream_to_vector(gr.sizeof_gr_complex*1, 4096)
+
+
+
+        ##################################################
+        # Connections
+        ##################################################
+        self.connect((self.blocks_stream_to_vector_1, 0), (self.probSigVec, 0))
+        self.connect((self.rtlsdr_source_0, 0), (self.blocks_stream_to_vector_1, 0))
+
+    def get_prob_freq(self):
+        return self.prob_freq
+
+    def set_prob_freq(self, prob_freq):
+        self.prob_freq = prob_freq
+        self.set_center_freq(my_freq.work(self.prob_freq)[0])
+
+    def get_top_peaks_amount(self):
+        return self.top_peaks_amount
+
+    def set_top_peaks_amount(self, top_peaks_amount):
+        self.top_peaks_amount = top_peaks_amount
+
+    def get_samp_rate(self):
+        return self.samp_rate
+
+    def set_samp_rate(self, samp_rate):
+        self.samp_rate = samp_rate
+        self.rtlsdr_source_0.set_sample_rate(self.samp_rate)
+
+    def get_poll_rate(self):
+        return self.poll_rate
+
+    def set_poll_rate(self, poll_rate):
+        self.poll_rate = poll_rate
+
+    def get_num_points(self):
+        return self.num_points
+
+    def set_num_points(self, num_points):
+        self.num_points = num_points
+
+    def get_flag(self):
+        return self.flag
+
+    def set_flag(self, flag):
+        self.flag = flag
+
+    def get_decimation(self):
+        return self.decimation
+
+    def set_decimation(self, decimation):
+        self.decimation = decimation
+
+    def get_center_freq(self):
+        return self.center_freq
+
+    def set_center_freq(self, center_freq):
+        self.center_freq = center_freq
+        self.rtlsdr_source_0.set_center_freq(self.center_freq, 0)
+
+
+def main(top_block_cls=get_center_freq, options=None):
+    time.sleep(3)
+    tb = top_block_cls()
+    def sig_handler(sig=None, frame=None):
+        tb.stop()
+        tb.wait()
+        sys.exit(0)
+
+    signal.signal(signal.SIGINT, sig_handler)
+    signal.signal(signal.SIGTERM, sig_handler)
+
+    tb.start()
+    try:
+        input('Press Enter to quit: ')
+    except EOFError:
+        pass
+    tb.wait()
+
+
+if __name__ == '__main__':
+    main()

orange_scripts/main_5800.py

@@ -1,172 +0,0 @@
-from gnuradio import blocks, gr
-import sys
-import signal
-import compose_send_data_5800 as my_freq
-import osmosdr
-import time
-import threading
-import subprocess
-import os
-from common.runtime import load_root_env, resolve_hackrf_index
-
-
-load_root_env(__file__)
-
-def get_hack_id():
-    return resolve_hackrf_index('HACKID_5800', 'orange_scripts/main_5800.py')
-    serial_number = os.getenv('HACKID_5800')
-    pos = None
-    output = []
-    try:
-        command = 'lsusb -v -d 1d50:6089 | grep iSerial'
-        output.append(subprocess.check_output(command, shell=True, text=True))
-    except subprocess.CalledProcessError as e:
-        print(f"Команда завершилась с кодом возврата {e.returncode}")
-        print(e)
-    print(output)
-    output_lines = output[0].strip().split('\n')
-    print(output_lines)
-    serial_numbers = [line.split()[-1] for line in output_lines]
-    print(serial_numbers)
-    for i, number in enumerate(serial_numbers):
-        if number == serial_number:
-            id = i
-            break
-    if id is not None:
-        print('HackId is: {0}'.format(id))
-        return str(id)
-    else:
-        print('Такого хака нет!')
-
-
-class get_center_freq(gr.top_block):
-
-    def __init__(self):
-        gr.top_block.__init__(self, "get_center_freq")
-
-        ##################################################
-        # Variables
-        ##################################################
-        self.prob_freq = prob_freq = 0
-        self.top_peaks_amount = top_peaks_amount = 20
-        self.samp_rate = samp_rate = 20e6
-        self.poll_rate = poll_rate = 10000
-        self.num_points = num_points = 8192
-        self.flag = flag = 1
-        self.decimation = decimation = 1
-        self.center_freq = center_freq = my_freq.work(prob_freq)[0]
-
-        ##################################################
-        # Blocks
-        ##################################################
-        self.probSigVec = blocks.probe_signal_vc(4096)
-        self.rtlsdr_source_0 = osmosdr.source(
-            args="numchan=" + str(1) + " " + 'hackrf=' + get_hack_id()
-        )
-        self.rtlsdr_source_0.set_time_unknown_pps(osmosdr.time_spec_t())
-        self.rtlsdr_source_0.set_sample_rate(samp_rate)
-        self.rtlsdr_source_0.set_center_freq(center_freq, 0)
-        self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(16, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 0)
-        self.rtlsdr_source_0.set_bb_gain(0, 0)
-        self.rtlsdr_source_0.set_antenna('', 0)
-        self.rtlsdr_source_0.set_bandwidth(0, 0)
-        self.rtlsdr_source_0.set_min_output_buffer(4096)
-
-        def _prob_freq_probe():
-            while True:
-
-                val = self.probSigVec.level()
-                try:
-                    self.set_prob_freq(val)
-                except AttributeError:
-                    pass
-                time.sleep(1.0 / (poll_rate))
-        _prob_freq_thread = threading.Thread(target=_prob_freq_probe)
-        _prob_freq_thread.daemon = True
-        _prob_freq_thread.start()
-
-        self.blocks_stream_to_vector_1 = blocks.stream_to_vector(gr.sizeof_gr_complex*1, 4096)
-
-
-
-        ##################################################
-        # Connections
-        ##################################################
-        self.connect((self.blocks_stream_to_vector_1, 0), (self.probSigVec, 0))
-        self.connect((self.rtlsdr_source_0, 0), (self.blocks_stream_to_vector_1, 0))
-
-    def get_prob_freq(self):
-        return self.prob_freq
-
-    def set_prob_freq(self, prob_freq):
-        self.prob_freq = prob_freq
-        self.set_center_freq(my_freq.work(self.prob_freq)[0])
-
-    def get_top_peaks_amount(self):
-        return self.top_peaks_amount
-
-    def set_top_peaks_amount(self, top_peaks_amount):
-        self.top_peaks_amount = top_peaks_amount
-
-    def get_samp_rate(self):
-        return self.samp_rate
-
-    def set_samp_rate(self, samp_rate):
-        self.samp_rate = samp_rate
-        self.rtlsdr_source_0.set_sample_rate(self.samp_rate)
-
-    def get_poll_rate(self):
-        return self.poll_rate
-
-    def set_poll_rate(self, poll_rate):
-        self.poll_rate = poll_rate
-
-    def get_num_points(self):
-        return self.num_points
-
-    def set_num_points(self, num_points):
-        self.num_points = num_points
-
-    def get_flag(self):
-        return self.flag
-
-    def set_flag(self, flag):
-        self.flag = flag
-
-    def get_decimation(self):
-        return self.decimation
-
-    def set_decimation(self, decimation):
-        self.decimation = decimation
-
-    def get_center_freq(self):
-        return self.center_freq
-
-    def set_center_freq(self, center_freq):
-        self.center_freq = center_freq
-        self.rtlsdr_source_0.set_center_freq(self.center_freq, 0)
-
-
-def main(top_block_cls=get_center_freq, options=None):
-    time.sleep(3)
-    tb = top_block_cls()
-    def sig_handler(sig=None, frame=None):
-        tb.stop()
-        tb.wait()
-        sys.exit(0)
-
-    signal.signal(signal.SIGINT, sig_handler)
-    signal.signal(signal.SIGTERM, sig_handler)
-
-    tb.start()
-    try:
-        input('Press Enter to quit: ')
-    except EOFError:
-        pass
-    tb.wait()
-
-
-if __name__ == '__main__':
-    main()

read_energy.py

@@ -69,7 +69,7 @@ class ProbeTop(gr.top_block):
             try:
                 getattr(self.src, fn)(val, 0)
             except Exception:
-                raise Exception("не ставится  усиление")
+                pass
         try:
             self.src.set_bandwidth(0, 0)
         except Exception:
@@ -260,7 +260,7 @@ def build_parser() -> argparse.ArgumentParser:
     p.add_argument("--interval", type=float, default=0.5, help="Per-device read interval (s)")
     p.add_argument("--refresh", type=float, default=0.5, help="Console refresh interval (s)")
     p.add_argument("--reopen-delay", type=float, default=1.0, help="Retry delay after BUSY/ERR (s)")
-    p.add_argument("--gain", type=float, default=0.0, help="General gain")
+    p.add_argument("--gain", type=float, default=16.0, help="General gain")
     p.add_argument("--if-gain", type=float, default=16.0, help="IF gain")
     p.add_argument("--bb-gain", type=float, default=16.0, help="BB gain")
     return p

read_energy_wide.py

@@ -1,411 +1,3 @@
-"""
-./.venv-sdr/bin/python read_energy_wide.py \
-    --serial 0000000000000000a18c63dc2a83b813 \
-    --sample-rate 20000000 \
-    --base 6000 \
-    --roof 5700 \
-    --step 20
-
-"""
-#!/usr/bin/env python3
-import argparse
-import math
-import re
-import signal
-import subprocess
-import sys
-import time
-from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple
-
-try:
-    import numpy as np
-except Exception as exc:
-    print(f"numpy import failed: {exc}", file=sys.stderr)
-    sys.exit(1)
-
-try:
-    from gnuradio import blocks, gr
-    import osmosdr
-except Exception as exc:
-    print(f"gnuradio/osmosdr import failed: {exc}", file=sys.stderr)
-    print("Run with the SDR venv, e.g. .venv-sdr/bin/python read_energy_wide.py", file=sys.stderr)
-    sys.exit(1)
-
-EPS = 1e-20
-
-
-@dataclass
-class ScanWindow:
-    seq: int
-    start_mhz: float
-    end_mhz: float
-    low_mhz: float
-    high_mhz: float
-    center_mhz: float
-    status: str = "INIT"
-    rms: Optional[float] = None
-    power_lin: Optional[float] = None
-    dbfs: Optional[float] = None
-    samples: int = 0
-    updated_at: float = 0.0
-    error: str = ""
-    pass_no: int = 0
-
-
-class WideProbeTop(gr.top_block):
-    def __init__(
-        self,
-        index: int,
-        center_freq_hz: float,
-        sample_rate: float,
-        vec_len: int,
-        gain: float,
-        if_gain: float,
-        bb_gain: float,
-    ):
-        super().__init__("hackrf_energy_wide_probe")
-        self.probe = blocks.probe_signal_vc(vec_len)
-        self.stream_to_vec = blocks.stream_to_vector(gr.sizeof_gr_complex * 1, vec_len)
-        self.src = osmosdr.source(args=f"numchan=1 hackrf={index}")
-        self.src.set_time_unknown_pps(osmosdr.time_spec_t())
-        self.src.set_sample_rate(sample_rate)
-        self.src.set_center_freq(center_freq_hz, 0)
-        try:
-            self.src.set_freq_corr(0, 0)
-        except Exception:
-            pass
-        try:
-            self.src.set_gain_mode(False, 0)
-        except Exception:
-            pass
-        for fn, val in (("set_gain", gain), ("set_if_gain", if_gain), ("set_bb_gain", bb_gain)):
-            try:
-                getattr(self.src, fn)(val, 0)
-            except Exception:
-                pass
-        try:
-            self.src.set_bandwidth(0, 0)
-        except Exception:
-            pass
-        try:
-            self.src.set_antenna("", 0)
-        except Exception:
-            pass
-        self.connect((self.src, 0), (self.stream_to_vec, 0))
-        self.connect((self.stream_to_vec, 0), (self.probe, 0))
-
-    def tune(self, freq_hz: float) -> None:
-        self.src.set_center_freq(freq_hz, 0)
-
-    def read_metrics(self) -> Tuple[float, float, float, int]:
-        arr = np.asarray(self.probe.level(), dtype=np.complex64)
-        if arr.size == 0:
-            raise RuntimeError("no samples")
-        power_lin = float(np.mean(arr.real * arr.real + arr.imag * arr.imag))
-        rms = math.sqrt(max(power_lin, 0.0))
-        dbfs = 10.0 * math.log10(max(power_lin, EPS))
-        return rms, power_lin, dbfs, int(arr.size)
-
-    def read_window(self, settle: float, avg_reads: int, pause_between_reads: float) -> Tuple[float, float, float, int]:
-        if settle > 0:
-            time.sleep(settle)
-
-        read_count = max(1, avg_reads)
-        powers: List[float] = []
-        sample_sizes: List[int] = []
-        last_error: Optional[Exception] = None
-
-        for idx in range(read_count):
-            deadline = time.time() + 1.0
-            while True:
-                try:
-                    _, power_lin, _, samples = self.read_metrics()
-                    powers.append(power_lin)
-                    sample_sizes.append(samples)
-                    break
-                except Exception as exc:
-                    last_error = exc
-                    if time.time() >= deadline:
-                        raise RuntimeError(str(last_error) if last_error else "no samples")
-                    time.sleep(0.02)
-            if idx + 1 < read_count and pause_between_reads > 0:
-                time.sleep(pause_between_reads)
-
-        power_lin = float(sum(powers) / len(powers))
-        rms = math.sqrt(max(power_lin, 0.0))
-        dbfs = 10.0 * math.log10(max(power_lin, EPS))
-        samples = int(sum(sample_sizes) / len(sample_sizes))
-        return rms, power_lin, dbfs, samples
-
-
-def parse_hackrf_info() -> Dict[str, int]:
-    try:
-        proc = subprocess.run(["hackrf_info"], capture_output=True, text=True, timeout=15)
-    except FileNotFoundError:
-        raise RuntimeError("hackrf_info not found")
-    except subprocess.TimeoutExpired:
-        raise RuntimeError("hackrf_info timeout")
-    text = (proc.stdout or "") + "\n" + (proc.stderr or "")
-    out: Dict[str, int] = {}
-    cur_idx: Optional[int] = None
-    for line in text.splitlines():
-        m = re.search(r"^Index:\s*(\d+)", line)
-        if m:
-            cur_idx = int(m.group(1))
-            continue
-        m = re.search(r"^Serial number:\s*([0-9a-fA-F]+)", line)
-        if m and cur_idx is not None:
-            out[m.group(1).lower()] = cur_idx
-    if not out:
-        raise RuntimeError("no devices parsed from hackrf_info")
-    return out
-
-
-def fmt(value: Optional[float], spec: str) -> str:
-    return "-" if value is None else format(value, spec)
-
-
-def build_windows(base_mhz: float, roof_mhz: float, step_mhz: float) -> List[ScanWindow]:
-    if step_mhz <= 0:
-        raise ValueError("step must be > 0")
-    if base_mhz == roof_mhz:
-        raise ValueError("base and roof must be different")
-
-    direction = -1.0 if roof_mhz < base_mhz else 1.0
-    edge = base_mhz
-    seq = 1
-    windows: List[ScanWindow] = []
-
-    while True:
-        next_edge = edge + direction * step_mhz
-        if direction < 0 and next_edge < roof_mhz:
-            next_edge = roof_mhz
-        if direction > 0 and next_edge > roof_mhz:
-            next_edge = roof_mhz
-
-        low_mhz = min(edge, next_edge)
-        high_mhz = max(edge, next_edge)
-        center_mhz = (low_mhz + high_mhz) / 2.0
-        windows.append(
-            ScanWindow(
-                seq=seq,
-                start_mhz=edge,
-                end_mhz=next_edge,
-                low_mhz=low_mhz,
-                high_mhz=high_mhz,
-                center_mhz=center_mhz,
-            )
-        )
-
-        if next_edge == roof_mhz:
-            break
-        edge = next_edge
-        seq += 1
-
-    return windows
-
-
-def render(
-    windows: List[ScanWindow],
-    serial: str,
-    index: int,
-    sample_rate: float,
-    base_mhz: float,
-    roof_mhz: float,
-    step_mhz: float,
-    started_at: float,
-    pass_no: int,
-    current_seq: int,
-) -> None:
-    now = time.time()
-    capture_bw_mhz = sample_rate / 1e6
-    current_row = next((row for row in windows if row.seq == current_seq), None)
-    best_row = max(
-        (row for row in windows if row.status == "OK" and row.dbfs is not None),
-        key=lambda row: row.dbfs if row.dbfs is not None else float("-inf"),
-        default=None,
-    )
-    print("\x1b[2J\x1b[H", end="")
-    print("HackRF Wide Energy Monitor (relative power: RMS / linear / dBFS)")
-    print(
-        f"serial: {serial} | idx: {index} | sample-rate: {capture_bw_mhz:.3f} MHz | "
-        f"scan: {base_mhz:.3f}->{roof_mhz:.3f} MHz step {step_mhz:.3f} MHz | "
-        f"pass: {pass_no} | uptime: {int(now-started_at)}s | {time.strftime('%Y-%m-%d %H:%M:%S')}"
-    )
-    print()
-    header = (
-        f"{'cur':>3} {'seq':>3} {'window MHz':>23} {'center':>9} {'status':>8} "
-        f"{'dBFS':>9} {'rms':>10} {'power':>12} {'N':>5} {'age':>5}  error"
-    )
-    print(header)
-    print("-" * len(header))
-    for row in windows:
-        age = "-" if row.updated_at <= 0 else f"{(now-row.updated_at):.1f}"
-        err = row.error
-        if len(err) > 50:
-            err = err[:47] + "..."
-        marker = ">>>" if row.seq == current_seq else ""
-        print(
-            f"{marker:>3} {row.seq:>3} "
-            f"{f'{row.high_mhz:.3f}-{row.low_mhz:.3f}':>23} {row.center_mhz:>9.3f} {row.status:>8} "
-            f"{fmt(row.dbfs, '.2f'):>9} {fmt(row.rms, '.6f'):>10} {fmt(row.power_lin, '.8f'):>12} "
-            f"{row.samples:>5} {age:>5}  {err}"
-        )
-    print()
-    if best_row is not None:
-        best_age = "-" if best_row.updated_at <= 0 else f"{(now-best_row.updated_at):.1f}"
-        print(
-            f"{'':>3} {'MAX':>3} "
-            f"{f'{best_row.high_mhz:.3f}-{best_row.low_mhz:.3f}':>23} {best_row.center_mhz:>9.3f} {best_row.status:>8} "
-            f"{fmt(best_row.dbfs, '.2f'):>9} {fmt(best_row.rms, '.6f'):>10} {fmt(best_row.power_lin, '.8f'):>12} "
-            f"{best_row.samples:>5} {best_age:>5}  pass={best_row.pass_no}"
-        )
-    elif current_row is not None:
-        current_age = "-" if current_row.updated_at <= 0 else f"{(now-current_row.updated_at):.1f}"
-        print(
-            f"{'':>3} {'MAX':>3} "
-            f"{f'{current_row.high_mhz:.3f}-{current_row.low_mhz:.3f}':>23} {current_row.center_mhz:>9.3f} {'INIT':>8} "
-            f"{fmt(None, '.2f'):>9} {fmt(None, '.6f'):>10} {fmt(None, '.8f'):>12} "
-            f"{0:>5} {current_age:>5}  no successful windows yet"
-        )
-    print("Ctrl+C to stop. Window width equals step; sample-rate must be >= step to cover each window.")
-    sys.stdout.flush()
-
-
-def build_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(description="Retune one HackRF across a wide frequency range and measure energy")
-    parser.add_argument("--serial", required=True, help="HackRF serial number from hackrf_info")
-    parser.add_argument("--sample-rate", type=float, required=True, help="Sample rate in Hz")
-    parser.add_argument("--base", type=float, required=True, help="Scan start edge in MHz")
-    parser.add_argument("--roof", type=float, required=True, help="Scan end edge in MHz")
-    parser.add_argument("--step", type=float, required=True, help="Window width / retune step in MHz")
-    parser.add_argument("--vec-len", type=int, default=4096, help="Probe vector length")
-    parser.add_argument("--settle", type=float, default=0.12, help="Wait time after retune before reading (s)")
-    parser.add_argument("--avg-reads", type=int, default=3, help="How many probe reads to average per window")
-    parser.add_argument("--pause-between-reads", type=float, default=0.02, help="Pause between averaged reads (s)")
-    parser.add_argument("--passes", type=int, default=0, help="Number of sweep passes, 0 means infinite")
-    parser.add_argument("--gain", type=float, default=16.0, help="General gain")
-    parser.add_argument("--if-gain", type=float, default=16.0, help="IF gain")
-    parser.add_argument("--bb-gain", type=float, default=16.0, help="BB gain")
-    return parser
-
-
-def main() -> int:
-    args = build_parser().parse_args()
-    serial = args.serial.lower()
-
-    try:
-        windows = build_windows(args.base, args.roof, args.step)
-    except ValueError as exc:
-        print(f"invalid scan range: {exc}", file=sys.stderr)
-        return 2
-
-    step_hz = args.step * 1e6
-    if args.sample_rate < step_hz:
-        print(
-            f"sample-rate {args.sample_rate:.0f} Hz is smaller than step window {step_hz:.0f} Hz; "
-            "this would leave gaps in the scan",
-            file=sys.stderr,
-        )
-        return 2
-
-    try:
-        serial_to_index = parse_hackrf_info()
-    except Exception as exc:
-        print(f"hackrf discovery failed: {exc}", file=sys.stderr)
-        return 3
-
-    index = serial_to_index.get(serial)
-    if index is None:
-        print(f"serial {serial} not found in hackrf_info", file=sys.stderr)
-        print("available serials:", file=sys.stderr)
-        for item_serial, item_index in sorted(serial_to_index.items(), key=lambda item: item[1]):
-            print(f"  idx={item_index} serial={item_serial}", file=sys.stderr)
-        return 4
-
-    stop_requested = False
-
-    def on_signal(signum, frame):
-        nonlocal stop_requested
-        stop_requested = True
-
-    signal.signal(signal.SIGINT, on_signal)
-    signal.signal(signal.SIGTERM, on_signal)
-
-    probe: Optional[WideProbeTop] = None
-    started_at = time.time()
-    pass_no = 0
-    current_seq = windows[0].seq
-
-    try:
-        probe = WideProbeTop(
-            index=index,
-            center_freq_hz=windows[0].center_mhz * 1e6,
-            sample_rate=args.sample_rate,
-            vec_len=args.vec_len,
-            gain=args.gain,
-            if_gain=args.if_gain,
-            bb_gain=args.bb_gain,
-        )
-        probe.start()
-        time.sleep(max(args.settle, 0.12))
-
-        while not stop_requested:
-            pass_no += 1
-            for row in windows:
-                if stop_requested:
-                    break
-                current_seq = row.seq
-                try:
-                    probe.tune(row.center_mhz * 1e6)
-                    rms, power_lin, dbfs, samples = probe.read_window(
-                        settle=args.settle,
-                        avg_reads=args.avg_reads,
-                        pause_between_reads=args.pause_between_reads,
-                    )
-                    row.status = "OK"
-                    row.rms = rms
-                    row.power_lin = power_lin
-                    row.dbfs = dbfs
-                    row.samples = samples
-                    row.error = ""
-                    row.updated_at = time.time()
-                    row.pass_no = pass_no
-                except Exception as exc:
-                    row.status = "ERR"
-                    row.error = str(exc)
-                    row.updated_at = time.time()
-                render(
-                    windows=windows,
-                    serial=serial,
-                    index=index,
-                    sample_rate=args.sample_rate,
-                    base_mhz=args.base,
-                    roof_mhz=args.roof,
-                    step_mhz=args.step,
-                    started_at=started_at,
-                    pass_no=pass_no,
-                    current_seq=current_seq,
-                )
-            if args.passes > 0 and pass_no >= args.passes:
-                break
-    except Exception as exc:
-        print(f"scanner failed: {exc}", file=sys.stderr)
-        return 5
-    finally:
-        if probe is not None:
-            try:
-                probe.stop()
-                probe.wait()
-            except Exception:
-                pass
-
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
 #!/usr/bin/env python3
 import argparse
 import math

restart_all.sh

@@ -7,6 +7,8 @@ COMPOSE_FILE="${PROJECT_ROOT}/deploy/docker/docker-compose.yml"
 SDR_UNITS=(
   dronedetector-sdr-433.service
   dronedetector-sdr-750.service
+  #dronedetector-sdr-868-915.service
+  #dronedetector-sdr-868.service
   dronedetector-sdr-1500.service
   dronedetector-sdr-3300.service
   dronedetector-sdr-4500.service
@@ -17,51 +19,10 @@ SDR_UNITS=(
   dronedetector-sdr-2400.service
 )
 
-CONFIGURED_SDR_UNITS=()
-
 log() {
   printf '[restart_all] %s\n' "$*"
 }
 
-sdr_unit_env_key() {
-  local unit="$1"
-  local band="${unit#dronedetector-sdr-}"
-  band="${band%.service}"
-  printf 'hack_%s\n' "$band"
-}
-
-get_env_value() {
-  local key="$1"
-  awk -F= -v key="$key" '
-    $1 == key {
-      value = substr($0, index($0, "=") + 1)
-      sub(/^[[:space:]]+/, "", value)
-      sub(/[[:space:]]+$/, "", value)
-      gsub(/^"/, "", value)
-      gsub(/"$/, "", value)
-      gsub(/^'\''/, "", value)
-      gsub(/'\''$/, "", value)
-      print value
-      exit
-    }
-  ' "${PROJECT_ROOT}/.env"
-}
-
-populate_configured_sdr_units() {
-  CONFIGURED_SDR_UNITS=()
-
-  local unit env_key env_value
-  for unit in "${SDR_UNITS[@]}"; do
-    env_key="$(sdr_unit_env_key "$unit")"
-    env_value="$(get_env_value "$env_key")"
-    if [[ -n "$env_value" ]]; then
-      CONFIGURED_SDR_UNITS+=("$unit")
-    else
-      log "Skipping ${unit}: ${env_key} is empty in .env"
-    fi
-  done
-}
-
 if [[ -x /usr/bin/sudo ]]; then
   SUDO=(sudo)
 else
@@ -80,7 +41,7 @@ restart_docker_services() {
 
 restart_sdr_services() {
   log "Restarting SDR systemd units"
-  for unit in "${CONFIGURED_SDR_UNITS[@]}"; do
+  for unit in "${SDR_UNITS[@]}"; do
     ${SUDO[@]} systemctl restart "$unit"
   done
 }
@@ -90,7 +51,7 @@ print_status() {
   docker compose -f "$COMPOSE_FILE" ps || true
 
   log "SDR status"
-  for unit in "${CONFIGURED_SDR_UNITS[@]}"; do
+  for unit in "${SDR_UNITS[@]}"; do
     if ${SUDO[@]} systemctl is-active --quiet "$unit"; then
       printf '%s: active\n' "$unit"
     else
@@ -100,7 +61,6 @@ print_status() {
 }
 
 main() {
-  populate_configured_sdr_units
   restart_docker_services
   restart_sdr_services
   print_status

src/main_1200.py

@@ -40,8 +40,8 @@ class get_center_freq(gr.top_block):
         self.rtlsdr_source_0.set_sample_rate(self.samp_rate)
         self.rtlsdr_source_0.set_center_freq(self.center_freq, 0)
         self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(16, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 0)
+        self.rtlsdr_source_0.set_gain(24, 0)
+        self.rtlsdr_source_0.set_if_gain(24, 0)
         self.rtlsdr_source_0.set_bb_gain(100, 0)
         self.rtlsdr_source_0.set_antenna('', 0)
         self.rtlsdr_source_0.set_bandwidth(0, 0)

src/main_1500.py

@@ -40,8 +40,8 @@ class get_center_freq(gr.top_block):
         self.rtlsdr_source_0.set_sample_rate(self.samp_rate)
         self.rtlsdr_source_0.set_center_freq(self.center_freq, 0)
         self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(16, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 0)
+        self.rtlsdr_source_0.set_gain(24, 0)
+        self.rtlsdr_source_0.set_if_gain(24, 0)
         self.rtlsdr_source_0.set_bb_gain(100, 0)
         self.rtlsdr_source_0.set_antenna('', 0)
         self.rtlsdr_source_0.set_bandwidth(0, 0)

src/main_2400.py

@@ -40,8 +40,8 @@ class get_center_freq(gr.top_block):
         self.rtlsdr_source_0.set_sample_rate(self.samp_rate)
         self.rtlsdr_source_0.set_center_freq(self.center_freq, 0)
         self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(16, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 0)
+        self.rtlsdr_source_0.set_gain(24, 0)
+        self.rtlsdr_source_0.set_if_gain(24, 0)
         self.rtlsdr_source_0.set_bb_gain(100, 0)
         self.rtlsdr_source_0.set_antenna('', 0)
         self.rtlsdr_source_0.set_bandwidth(0, 0)

src/main_3300.py

@@ -94,8 +94,8 @@ class get_center_freq(gr.top_block):
         self.rtlsdr_source_0.set_sample_rate(samp_rate)
         self.rtlsdr_source_0.set_center_freq(center_freq, 0)
         self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(16, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 0)
+        self.rtlsdr_source_0.set_gain(100, 0)
+        self.rtlsdr_source_0.set_if_gain(100, 0)
         self.rtlsdr_source_0.set_bb_gain(100, 0)
         self.rtlsdr_source_0.set_antenna('', 0)
         self.rtlsdr_source_0.set_bandwidth(0, 0)

src/main_433.py

@@ -94,8 +94,8 @@ class get_center_freq(gr.top_block):
         self.rtlsdr_source_0.set_sample_rate(samp_rate)
         self.rtlsdr_source_0.set_center_freq(center_freq, 0)
         self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(16, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 0)
+        self.rtlsdr_source_0.set_gain(24, 0)
+        self.rtlsdr_source_0.set_if_gain(24, 0)
         self.rtlsdr_source_0.set_bb_gain(100, 0)
         self.rtlsdr_source_0.set_antenna('', 0)
         self.rtlsdr_source_0.set_bandwidth(0, 0)

src/main_4500.py

@@ -94,8 +94,8 @@ class get_center_freq(gr.top_block):
         self.rtlsdr_source_0.set_sample_rate(samp_rate)
         self.rtlsdr_source_0.set_center_freq(center_freq, 0)
         self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(16, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 0)
+        self.rtlsdr_source_0.set_gain(100, 0)
+        self.rtlsdr_source_0.set_if_gain(100, 0)
         self.rtlsdr_source_0.set_bb_gain(100, 0)
         self.rtlsdr_source_0.set_antenna('', 0)
         self.rtlsdr_source_0.set_bandwidth(0, 0)

src/main_5200.py

@@ -93,8 +93,8 @@ class get_center_freq(gr.top_block):
         self.rtlsdr_source_0.set_sample_rate(samp_rate)
         self.rtlsdr_source_0.set_center_freq(center_freq, 0)
         self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(16, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 0)
+        self.rtlsdr_source_0.set_gain(100, 0)
+        self.rtlsdr_source_0.set_if_gain(100, 0)
         self.rtlsdr_source_0.set_bb_gain(100, 0)
         self.rtlsdr_source_0.set_antenna('', 0)
         self.rtlsdr_source_0.set_bandwidth(0, 0)

src/main_5800.py

@@ -94,8 +94,8 @@ class get_center_freq(gr.top_block):
         self.rtlsdr_source_0.set_sample_rate(samp_rate)
         self.rtlsdr_source_0.set_center_freq(center_freq, 0)
         self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(16, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 0)
+        self.rtlsdr_source_0.set_gain(100, 0)
+        self.rtlsdr_source_0.set_if_gain(100, 0)
         self.rtlsdr_source_0.set_bb_gain(100, 0)
         self.rtlsdr_source_0.set_antenna('', 0)
         self.rtlsdr_source_0.set_bandwidth(0, 0)

src/main_750.py

@@ -94,8 +94,8 @@ class get_center_freq(gr.top_block):
         self.rtlsdr_source_0.set_sample_rate(samp_rate)
         self.rtlsdr_source_0.set_center_freq(center_freq, 0)
         self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(16, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 0)
+        self.rtlsdr_source_0.set_gain(24, 0)
+        self.rtlsdr_source_0.set_if_gain(24, 0)
         self.rtlsdr_source_0.set_bb_gain(100, 0)
         self.rtlsdr_source_0.set_antenna('', 0)
         self.rtlsdr_source_0.set_bandwidth(0, 0)

src/main_868.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+from gnuradio import blocks
+from gnuradio import gr
+from gnuradio import zeromq
+import signal
+import sys
+import threading
+import time
+
+import embedded_868 as my_freq
+
+from common.runtime import load_root_env
+from common.shared_stream_addrs import SHARED_868_ADDR, SHARED_VECTOR_LEN
+
+
+load_root_env(__file__)
+
+
+class get_center_freq(gr.top_block):
+    def __init__(self):
+        gr.top_block.__init__(self, 'get_center_freq')
+
+        self.prob_freq = 0
+        self.poll_rate = 10000
+        self.vector_len = SHARED_VECTOR_LEN
+        self.center_freq = 0
+        self.shared_addr = SHARED_868_ADDR
+        self._stop_polling = threading.Event()
+        self._prob_freq_thread = None
+
+        self.probSigVec = blocks.probe_signal_vc(self.vector_len)
+        self.shared_source_0 = zeromq.pull_source(
+            gr.sizeof_gr_complex,
+            self.vector_len,
+            self.shared_addr,
+            100,
+            False,
+            -1,
+            False,
+        )
+        self.connect((self.shared_source_0, 0), (self.probSigVec, 0))
+
+    def start_polling(self):
+        if self._prob_freq_thread is not None:
+            return
+
+        def _prob_freq_probe():
+            while not self._stop_polling.is_set():
+                self.set_prob_freq(self.probSigVec.level())
+                time.sleep(1.0 / self.poll_rate)
+
+        self._prob_freq_thread = threading.Thread(target=_prob_freq_probe, daemon=True)
+        self._prob_freq_thread.start()
+
+    def get_prob_freq(self):
+        return self.prob_freq
+
+    def set_prob_freq(self, prob_freq):
+        self.prob_freq = prob_freq
+        self.center_freq = my_freq.work(self.prob_freq)
+
+    def get_center_freq(self):
+        return self.center_freq
+
+    def set_center_freq(self, center_freq):
+        self.center_freq = center_freq
+
+    def close(self):
+        self._stop_polling.set()
+        self.stop()
+        self.wait()
+
+
+def main(top_block_cls=get_center_freq, options=None):
+    tb = top_block_cls()
+
+    def sig_handler(sig=None, frame=None):
+        tb.close()
+        sys.exit(0)
+
+    signal.signal(signal.SIGINT, sig_handler)
+    signal.signal(signal.SIGTERM, sig_handler)
+
+    tb.start()
+    tb.start_polling()
+    try:
+        print('shared_pull_addr:', SHARED_868_ADDR)
+        print('debug_flag:', my_freq.debug_flag)
+        print('save_data_flag:', my_freq.save_data_flag)
+        print('send_to_module_flag:', my_freq.send_to_module_flag)
+    except EOFError:
+        pass
+    tb.wait()
+
+
+if __name__ == '__main__':
+    main()

src/main_868_915_router.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+from gnuradio import blocks
+from gnuradio import gr
+import signal
+import sys
+import threading
+import time
+
+import osmosdr
+
+from common.runtime import load_root_env, resolve_hackrf_index
+from common.shared_stream_addrs import SHARED_VECTOR_LEN
+from shared_router_868_915 import SharedRouter868915
+
+
+load_root_env(__file__)
+
+
+def get_hack_id():
+    return resolve_hackrf_index('hack_868', 'src/main_868_915_router.py')
+
+
+class get_center_freq(gr.top_block):
+    def __init__(self):
+        gr.top_block.__init__(self, 'get_center_freq')
+
+        self.prob_freq = 0
+        self.poll_rate = 10000
+        self.vector_len = SHARED_VECTOR_LEN
+        self.router = SharedRouter868915()
+        self.active_lane = self.router.get_active_name()
+        self.center_freq = self.router.get_start_freq()
+        self._stop_polling = threading.Event()
+        self._prob_freq_thread = None
+
+        self.probSigVec = blocks.probe_signal_vc(self.vector_len)
+        self.rtlsdr_source_0 = osmosdr.source(
+            args='numchan=' + str(1) + ' ' + 'hackrf=' + get_hack_id()
+        )
+        self.rtlsdr_source_0.set_time_unknown_pps(osmosdr.time_spec_t())
+        self.rtlsdr_source_0.set_freq_corr(0, 0)
+        self.rtlsdr_source_0.set_antenna('', 0)
+        self.rtlsdr_source_0.set_min_output_buffer(self.vector_len)
+        self.apply_active_frontend()
+        self.rtlsdr_source_0.set_center_freq(self.center_freq, 0)
+
+        self.blocks_stream_to_vector_1 = blocks.stream_to_vector(gr.sizeof_gr_complex * 1, self.vector_len)
+        self.connect((self.rtlsdr_source_0, 0), (self.blocks_stream_to_vector_1, 0))
+        self.connect((self.blocks_stream_to_vector_1, 0), (self.probSigVec, 0))
+
+    def start_polling(self):
+        if self._prob_freq_thread is not None:
+            return
+
+        def _prob_freq_probe():
+            while not self._stop_polling.is_set():
+                self.set_prob_freq(self.probSigVec.level())
+                time.sleep(1.0 / self.poll_rate)
+
+        self._prob_freq_thread = threading.Thread(target=_prob_freq_probe, daemon=True)
+        self._prob_freq_thread.start()
+
+    def apply_active_frontend(self):
+        frontend = self.router.get_active_frontend()
+        self.rtlsdr_source_0.set_sample_rate(frontend['sample_rate'])
+        self.rtlsdr_source_0.set_gain(frontend['gain'], 0)
+        self.rtlsdr_source_0.set_if_gain(frontend['if_gain'], 0)
+        self.rtlsdr_source_0.set_bb_gain(frontend['bb_gain'], 0)
+        self.rtlsdr_source_0.set_bandwidth(frontend['bandwidth'], 0)
+
+    def get_prob_freq(self):
+        return self.prob_freq
+
+    def set_prob_freq(self, prob_freq):
+        self.prob_freq = prob_freq
+        next_center, lane_switched = self.router.route_vector(self.prob_freq)
+        if lane_switched:
+            self.active_lane = self.router.get_active_name()
+            self.apply_active_frontend()
+        if next_center != self.center_freq:
+            self.set_center_freq(next_center)
+
+    def get_center_freq(self):
+        return self.center_freq
+
+    def set_center_freq(self, center_freq):
+        self.center_freq = center_freq
+        self.rtlsdr_source_0.set_center_freq(self.center_freq, 0)
+
+    def close(self):
+        self._stop_polling.set()
+        try:
+            self.router.close()
+        finally:
+            self.stop()
+            self.wait()
+
+
+def main(top_block_cls=get_center_freq, options=None):
+    tb = top_block_cls()
+
+    def sig_handler(sig=None, frame=None):
+        tb.close()
+        sys.exit(0)
+
+    signal.signal(signal.SIGINT, sig_handler)
+    signal.signal(signal.SIGTERM, sig_handler)
+
+    tb.start()
+    tb.start_polling()
+    try:
+        print('shared_router_active_lane:', tb.router.get_active_name())
+        print('shared_router_start_freq:', tb.get_center_freq())
+    except EOFError:
+        pass
+    tb.wait()
+
+
+if __name__ == '__main__':
+    main()

src/main_915.py

@@ -40,8 +40,8 @@ class get_center_freq(gr.top_block):
         self.rtlsdr_source_0.set_sample_rate(self.samp_rate)
         self.rtlsdr_source_0.set_center_freq(self.center_freq, 0)
         self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(16, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 0)
+        self.rtlsdr_source_0.set_gain(24, 0)
+        self.rtlsdr_source_0.set_if_gain(24, 0)
         self.rtlsdr_source_0.set_bb_gain(100, 0)
         self.rtlsdr_source_0.set_antenna('', 0)
         self.rtlsdr_source_0.set_bandwidth(0, 0)

src/shared_router_868_915.py

@@ -0,0 +1,173 @@
+import os
+
+import numpy as np
+import zmq
+
+from common.runtime import load_root_env
+from common.shared_stream_addrs import SHARED_868_ADDR, SHARED_915_ADDR
+from core.multichannelswitcher import MultiChannel
+from core.sig_n_medi_collect import Signal
+
+
+load_root_env(__file__)
+
+
+class Scheduler868:
+    def __init__(self):
+        self.signal_length = int(os.getenv('signal_length_868'))
+        self.multi_channel = MultiChannel(
+            [*map(float, os.getenv('f_step_868').split())],
+            [*map(float, os.getenv('f_bases_868').split())],
+            [*map(float, os.getenv('f_roofs_868').split())],
+        )
+        self.base_freq = float(self.multi_channel.init_f())
+        self.signal = Signal()
+
+    def get_current_freq(self):
+        return float(self.multi_channel.get_cur_channel())
+
+    def process(self, lvl):
+        current_before = self.get_current_freq()
+        metric = self.signal.fill_signal(lvl, self.signal_length)
+        if metric == 0:
+            return current_before, False
+
+        next_freq = float(self.multi_channel.change_channel())
+        self.signal.clear()
+        lane_complete = next_freq == self.base_freq and current_before != self.base_freq
+        return next_freq, lane_complete
+
+
+class Scheduler915:
+    def __init__(self):
+        self.porog = float(os.getenv('POROG_915'))
+        self.point_amount = 100_000
+        self.split_size = 400_000
+        self.show_amount = int(0.8 * self.point_amount)
+        self.f_base = 0.91e9
+        self.f_step = 20e6
+        self.f_roof = 0.98e9
+        self.f = self.f_base
+        self.channel = 1
+        self.flag = 0
+        self.signal_arr = np.array([], dtype=np.complex64)
+
+    def get_current_freq(self):
+        return float(self.f)
+
+    def _median(self, sig):
+        samples = np.asarray(np.abs(np.array(sig, dtype=np.complex64)), dtype=np.float32)
+        sorted_samples = sorted(samples)
+        median = abs(float(np.median(sorted_samples[self.show_amount:])))
+        self.flag = 0 if self.porog > median else 1
+
+    def _advance(self):
+        next_freq = self.f + self.f_step
+        if next_freq >= self.f_roof:
+            self.f = self.f_base
+            self.channel = 1
+            return float(self.f), True
+
+        self.f = next_freq
+        self.channel += 1
+        return float(self.f), False
+
+    def process(self, lvl):
+        y = np.asarray(lvl, dtype=np.complex64).ravel()
+        self.signal_arr = np.concatenate((self.signal_arr, y), axis=None)
+
+        if self.flag == 0 and len(self.signal_arr) >= self.point_amount:
+            self._median(self.signal_arr[:self.point_amount])
+            self.signal_arr = np.array([], dtype=np.complex64)
+            if self.flag == 0:
+                return self._advance()
+
+        if len(self.signal_arr) >= self.split_size:
+            self.flag = 0
+            self.signal_arr = np.array([], dtype=np.complex64)
+            return self._advance()
+
+        return float(self.f), False
+
+
+class SharedRouter868915:
+    def __init__(self):
+        self.frontends = {
+            '868': {
+                'sample_rate': 20e6,
+                'gain': 24,
+                'if_gain': 24,
+                'bb_gain': 100,
+                'bandwidth': 0,
+            },
+            '915': {
+                'sample_rate': 20e6,
+                'gain': 16,
+                'if_gain': 16,
+                'bb_gain': 0,
+                'bandwidth': 0,
+            },
+        }
+        self.schedulers = {
+            '868': Scheduler868(),
+            '915': Scheduler915(),
+        }
+        self.context = zmq.Context.instance()
+        self.sockets = {
+            '868': self._make_socket(SHARED_868_ADDR),
+            '915': self._make_socket(SHARED_915_ADDR),
+        }
+        self.active_name = '868'
+        self.switch_counter = 0
+
+    def _make_socket(self, addr):
+        sock = self.context.socket(zmq.PUSH)
+        sock.setsockopt(zmq.SNDHWM, 64)
+        sock.setsockopt(zmq.LINGER, 0)
+        sock.setsockopt(zmq.IMMEDIATE, 1)
+        sock.bind(addr)
+        return sock
+
+    def close(self):
+        for sock in self.sockets.values():
+            try:
+                sock.close(0)
+            except Exception:
+                pass
+
+    def get_active_name(self):
+        return self.active_name
+
+    def get_active_freq(self):
+        return self.schedulers[self.active_name].get_current_freq()
+
+    def get_active_frontend(self):
+        return dict(self.frontends[self.active_name])
+
+    def get_start_freq(self):
+        return self.get_active_freq()
+
+    def route_vector(self, lvl):
+        vector = np.asarray(lvl, dtype=np.complex64).ravel()
+        active_name = self.active_name
+
+        try:
+            self.sockets[active_name].send(vector.tobytes(), zmq.NOBLOCK)
+        except zmq.Again:
+            pass
+
+        next_freq, lane_complete = self.schedulers[active_name].process(vector)
+        lane_switched = False
+
+        if lane_complete:
+            previous = self.active_name
+            self.active_name = '915' if self.active_name == '868' else '868'
+            next_freq = self.get_active_freq()
+            self.switch_counter += 1
+            lane_switched = True
+            print(
+                f'[shared-router-868-915] switch#{self.switch_counter}: '
+                f'{previous} -> {self.active_name}, tune={next_freq}'
+            )
+
+        return float(next_freq), lane_switched

train_scripts/ImageDatasetCreate_spec_imag_real_1200.ipynb

@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<contextlib.ExitStack at 0x272800ef510>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import io\n",
+    "import cv2\n",
+    "import copy\n",
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "import torch.nn as nn\n",
+    "import torch\n",
+    "import torchvision\n",
+    "from torch.utils.data import Dataset\n",
+    "from torch import default_generator, randperm\n",
+    "from PIL import Image\n",
+    "#from torch._utils import _accumulate\n",
+    "import csv\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "from scipy import ndimage\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)\n",
+    "batch_size = 16\n",
+    "momentum=0.9\n",
+    "lr = 1e-3\n",
+    "import random\n",
+    "sub_sample = 0.5\n",
+    "import matplotlib\n",
+    "import gc\n",
+    "import torchsig.utils as u\n",
+    "import torchsig.transforms.transforms as T\n",
+    "matplotlib.use('Agg')\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['agg.path.chunksize'] = 256*256\n",
+    "plt.ioff()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4848b066-2e09-4c1c-b8fa-8e3fa84d907a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = T.Spectrogram(nperseg=1024)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9267fbe1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sig2pic_with_spec(path_to_data, filename, specT=None,figsize=(16,16), dpi=16, resize = None):\n",
+    "    try:\n",
+    "        if specT is None:\n",
+    "            specT = T.Spectrogram(nperseg=1024)\n",
+    "        with open(path_to_data + filename, 'rb') as file:\n",
+    "            tmp = np.frombuffer(file.read(), dtype=np.complex64)\n",
+    "        signal = tmp\n",
+    "        spectr = np.array(specT(signal)['data']['samples'][:,:figsize[0] * dpi])\n",
+    "        mag = np.abs(signal)\n",
+    "        real = signal.real\n",
+    "\n",
+    "        fig2 = plt.figure(figsize = figsize)\n",
+    "        plt.axes(ylim=(-1, 1))\n",
+    "\n",
+    "        plt.plot(real, color='black')\n",
+    "        plt.gca().set_axis_off()\n",
+    "        plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
+    "        plt.margins(0,0)\n",
+    "        buf2 = io.BytesIO()\n",
+    "        fig2.savefig(buf2, format=\"png\", dpi=dpi)\n",
+    "        buf2.seek(0)\n",
+    "        img_arr2 = np.frombuffer(buf2.getvalue(), dtype=np.uint8)\n",
+    "        buf2.close()\n",
+    "        img2 = cv2.imdecode(img_arr2, 1)\n",
+    "        img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
+    "        plt.clf()\n",
+    "        plt.cla()\n",
+    "        plt.close()\n",
+    "        plt.close(fig2)\n",
+    "\n",
+    "        fig3 = plt.figure(figsize = figsize)\n",
+    "        plt.axes(ylim=(-1, 1))\n",
+    "\n",
+    "        plt.plot(mag, color='black')\n",
+    "        plt.gca().set_axis_off()\n",
+    "        plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
+    "        plt.margins(0,0)\n",
+    "        buf3 = io.BytesIO()\n",
+    "        fig3.savefig(buf3, format=\"png\", dpi=dpi)\n",
+    "        buf3.seek(0)\n",
+    "        img_arr3 = np.frombuffer(buf3.getvalue(), dtype=np.uint8)\n",
+    "        buf3.close()\n",
+    "        img3 = cv2.imdecode(img_arr3, 1)\n",
+    "        img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)\n",
+    "        plt.clf()\n",
+    "        plt.cla()\n",
+    "        plt.close()\n",
+    "        plt.close(fig3)\n",
+    "\n",
+    "        if resize != None:\n",
+    "            resized_real = cv2.resize(img2, resize)\n",
+    "            resized_mag = cv2.resize(img3, resize)\n",
+    "            resized_spectr = cv2.resize(spectr, resize)\n",
+    "            img = np.asarray([resized_real, resized_mag, resized_spectr], dtype=np.float32)\n",
+    "            return img\n",
+    "        img = np.asarray([img2, img3, spectr], dtype=np.float32)\n",
+    "        return img\n",
+    "    except Exception as e:\n",
+    "        print(str(e))\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_binaries = '//192.168.11.63/data/DATASETS/Energomash/1200'\n",
+    "path_to_pictures = '//192.168.11.63/data/DATASETS/Energomash/1200_jpg'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6f226f86-5d72-4573-8af6-750128b70263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                          | 0/963 [00:00<?, ?it/s]C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\scipy\\signal\\_spectral_py.py:1936: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = np.conjugate(result) * result\n",
+      "C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\scipy\\signal\\_spectral_py.py:1938: RuntimeWarning: invalid value encountered in multiply\n",
+      "  result *= scale\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████| 963/963 [17:40<00:00,  1.10s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  drone  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████| 963/963 [51:41<00:00,  3.22s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  noise  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "size = (256,256)\n",
+    "if not os.path.exists(path_to_pictures):\n",
+    "    os.mkdir(path_to_pictures)\n",
+    "for subdir in os.listdir(path_to_binaries):\n",
+    "    filepath = path_to_binaries + '/' + subdir + '/'\n",
+    "    if not os.path.exists(path_to_pictures +'/' + subdir):\n",
+    "        os.mkdir(path_to_pictures + '/' + subdir)\n",
+    "    files = os.listdir(filepath)\n",
+    "    for file in tqdm(files):\n",
+    "        savepath = path_to_pictures +'/' + subdir + '/' + file + '.npy'\n",
+    "        savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.png' \n",
+    "        savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.png' \n",
+    "        savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.png'\n",
+    "        if not os.path.exists(savepath):\n",
+    "            img = sig2pic_with_spec(path_to_data=filepath, filename=file, specT=s, resize = size)\n",
+    "            gc.collect()\n",
+    "            try:\n",
+    "                \n",
+    "                plt.imshow(img[0])\n",
+    "                plt.savefig(savepath_real_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "                plt.imshow(img[1])\n",
+    "                plt.savefig(savepath_imag_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "\n",
+    "                plt.imshow(img[2])\n",
+    "                plt.savefig(savepath_spec_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "                np.save(savepath, img)\n",
+    "                \n",
+    "            except Exception:\n",
+    "                continue\n",
+    "    print('Dir: ', subdir , ' finished!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f4bf849",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/ImageDatasetCreate_spec_imag_real_2400.ipynb

@@ -0,0 +1,270 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<contextlib.ExitStack at 0x25775eabcd0>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import io\n",
+    "import cv2\n",
+    "import copy\n",
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "import torch.nn as nn\n",
+    "import torch\n",
+    "import torchvision\n",
+    "from torch.utils.data import Dataset\n",
+    "from torch import default_generator, randperm\n",
+    "from PIL import Image\n",
+    "#from torch._utils import _accumulate\n",
+    "import csv\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "from scipy import ndimage\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)\n",
+    "batch_size = 16\n",
+    "momentum=0.9\n",
+    "lr = 1e-3\n",
+    "import random\n",
+    "sub_sample = 0.5\n",
+    "import matplotlib\n",
+    "import gc\n",
+    "import torchsig.utils as u\n",
+    "import torchsig.transforms.transforms as T\n",
+    "matplotlib.use('Agg')\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['agg.path.chunksize'] = 256*256\n",
+    "plt.ioff()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4848b066-2e09-4c1c-b8fa-8e3fa84d907a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = T.Spectrogram(nperseg=256)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9267fbe1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sig2pic_with_spec(path_to_data, filename, specT=None,figsize=(16,16), dpi=16, resize = None):\n",
+    "    try:\n",
+    "        if specT is None:\n",
+    "            specT = T.Spectrogram(nperseg=256)\n",
+    "        with open(path_to_data + filename, 'rb') as file:\n",
+    "            tmp = np.frombuffer(file.read(), dtype=np.complex64)\n",
+    "        signal = tmp\n",
+    "        spectr = np.array(specT(signal)['data']['samples'][:,:figsize[0] * dpi])\n",
+    "        mag = np.abs(signal)\n",
+    "        real = signal.real\n",
+    "\n",
+    "        fig2 = plt.figure(figsize = figsize)\n",
+    "        plt.axes(ylim=(-1, 1))\n",
+    "\n",
+    "        plt.plot(real, color='black')\n",
+    "        plt.gca().set_axis_off()\n",
+    "        plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
+    "        plt.margins(0,0)\n",
+    "        buf2 = io.BytesIO()\n",
+    "        fig2.savefig(buf2, format=\"png\", dpi=dpi)\n",
+    "        buf2.seek(0)\n",
+    "        img_arr2 = np.frombuffer(buf2.getvalue(), dtype=np.uint8)\n",
+    "        buf2.close()\n",
+    "        img2 = cv2.imdecode(img_arr2, 1)\n",
+    "        img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
+    "        plt.clf()\n",
+    "        plt.cla()\n",
+    "        plt.close()\n",
+    "        plt.close(fig2)\n",
+    "\n",
+    "        fig3 = plt.figure(figsize = figsize)\n",
+    "        plt.axes(ylim=(-1, 1))\n",
+    "\n",
+    "        plt.plot(mag, color='black')\n",
+    "        plt.gca().set_axis_off()\n",
+    "        plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
+    "        plt.margins(0,0)\n",
+    "        buf3 = io.BytesIO()\n",
+    "        fig3.savefig(buf3, format=\"png\", dpi=dpi)\n",
+    "        buf3.seek(0)\n",
+    "        img_arr3 = np.frombuffer(buf3.getvalue(), dtype=np.uint8)\n",
+    "        buf3.close()\n",
+    "        img3 = cv2.imdecode(img_arr3, 1)\n",
+    "        img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)\n",
+    "        plt.clf()\n",
+    "        plt.cla()\n",
+    "        plt.close()\n",
+    "        plt.close(fig3)\n",
+    "\n",
+    "        if resize != None:\n",
+    "            resized_real = cv2.resize(img2, resize)\n",
+    "            resized_mag = cv2.resize(img3, resize)\n",
+    "            resized_spectr = cv2.resize(spectr, resize)\n",
+    "            img = np.asarray([resized_real, resized_mag, resized_spectr], dtype=np.float32)\n",
+    "            return img\n",
+    "        img = np.asarray([img2, img3, spectr], dtype=np.float32)\n",
+    "        return img\n",
+    "    except Exception as e:\n",
+    "        print(str(e))\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_binaries = '//192.168.11.63/data/DATASETS/Energomash/2400'\n",
+    "path_to_pictures = '//192.168.11.63/data/DATASETS/Energomash/2400_jpg'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6f226f86-5d72-4573-8af6-750128b70263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                          | 0/965 [00:00<?, ?it/s]C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\scipy\\signal\\_spectral_py.py:1936: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = np.conjugate(result) * result\n",
+      "C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\scipy\\signal\\_spectral_py.py:1938: RuntimeWarning: invalid value encountered in multiply\n",
+      "  result *= scale\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████| 965/965 [28:11<00:00,  1.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  noise  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "size = (256,256)\n",
+    "if not os.path.exists(path_to_pictures):\n",
+    "    os.mkdir(path_to_pictures)\n",
+    "for subdir in os.listdir(path_to_binaries):\n",
+    "    filepath = path_to_binaries + '/' + subdir + '/'\n",
+    "    if not os.path.exists(path_to_pictures +'/' + subdir):\n",
+    "        os.mkdir(path_to_pictures + '/' + subdir)\n",
+    "    files = os.listdir(filepath)\n",
+    "    for file in tqdm(files):\n",
+    "        savepath = path_to_pictures +'/' + subdir + '/' + file + '.npy'\n",
+    "        savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.png' \n",
+    "        savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.png' \n",
+    "        savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.png'\n",
+    "        if not os.path.exists(savepath):\n",
+    "            img = sig2pic_with_spec(path_to_data=filepath, filename=file, specT=s, resize = size)\n",
+    "            gc.collect()\n",
+    "            try:\n",
+    "                \n",
+    "                plt.imshow(img[0])\n",
+    "                plt.savefig(savepath_real_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "                plt.imshow(img[1])\n",
+    "                plt.savefig(savepath_imag_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "\n",
+    "                plt.imshow(img[2])\n",
+    "                plt.savefig(savepath_spec_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "                np.save(savepath, img)\n",
+    "                \n",
+    "            except Exception:\n",
+    "                continue\n",
+    "    print('Dir: ', subdir , ' finished!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "106b1add",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/ImageDatasetCreate_spec_imag_real_5800.ipynb

@@ -0,0 +1,270 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<contextlib.ExitStack at 0x25775eabcd0>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import io\n",
+    "import cv2\n",
+    "import copy\n",
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "import torch.nn as nn\n",
+    "import torch\n",
+    "import torchvision\n",
+    "from torch.utils.data import Dataset\n",
+    "from torch import default_generator, randperm\n",
+    "from PIL import Image\n",
+    "#from torch._utils import _accumulate\n",
+    "import csv\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "from scipy import ndimage\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)\n",
+    "batch_size = 16\n",
+    "momentum=0.9\n",
+    "lr = 1e-3\n",
+    "import random\n",
+    "sub_sample = 0.5\n",
+    "import matplotlib\n",
+    "import gc\n",
+    "import torchsig.utils as u\n",
+    "import torchsig.transforms.transforms as T\n",
+    "matplotlib.use('Agg')\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['agg.path.chunksize'] = 256*256\n",
+    "plt.ioff()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4848b066-2e09-4c1c-b8fa-8e3fa84d907a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = T.Spectrogram(nperseg=256)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9267fbe1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sig2pic_with_spec(path_to_data, filename, specT=None,figsize=(16,16), dpi=16, resize = None):\n",
+    "    try:\n",
+    "        if specT is None:\n",
+    "            specT = T.Spectrogram(nperseg=256)\n",
+    "        with open(path_to_data + filename, 'rb') as file:\n",
+    "            tmp = np.frombuffer(file.read(), dtype=np.complex64)\n",
+    "        signal = tmp\n",
+    "        spectr = np.array(specT(signal)['data']['samples'][:,:figsize[0] * dpi])\n",
+    "        mag = np.abs(signal)\n",
+    "        real = signal.real\n",
+    "\n",
+    "        fig2 = plt.figure(figsize = figsize)\n",
+    "        plt.axes(ylim=(-1, 1))\n",
+    "\n",
+    "        plt.plot(real, color='black')\n",
+    "        plt.gca().set_axis_off()\n",
+    "        plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
+    "        plt.margins(0,0)\n",
+    "        buf2 = io.BytesIO()\n",
+    "        fig2.savefig(buf2, format=\"png\", dpi=dpi)\n",
+    "        buf2.seek(0)\n",
+    "        img_arr2 = np.frombuffer(buf2.getvalue(), dtype=np.uint8)\n",
+    "        buf2.close()\n",
+    "        img2 = cv2.imdecode(img_arr2, 1)\n",
+    "        img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
+    "        plt.clf()\n",
+    "        plt.cla()\n",
+    "        plt.close()\n",
+    "        plt.close(fig2)\n",
+    "\n",
+    "        fig3 = plt.figure(figsize = figsize)\n",
+    "        plt.axes(ylim=(-1, 1))\n",
+    "\n",
+    "        plt.plot(mag, color='black')\n",
+    "        plt.gca().set_axis_off()\n",
+    "        plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
+    "        plt.margins(0,0)\n",
+    "        buf3 = io.BytesIO()\n",
+    "        fig3.savefig(buf3, format=\"png\", dpi=dpi)\n",
+    "        buf3.seek(0)\n",
+    "        img_arr3 = np.frombuffer(buf3.getvalue(), dtype=np.uint8)\n",
+    "        buf3.close()\n",
+    "        img3 = cv2.imdecode(img_arr3, 1)\n",
+    "        img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)\n",
+    "        plt.clf()\n",
+    "        plt.cla()\n",
+    "        plt.close()\n",
+    "        plt.close(fig3)\n",
+    "\n",
+    "        if resize != None:\n",
+    "            resized_real = cv2.resize(img2, resize)\n",
+    "            resized_mag = cv2.resize(img3, resize)\n",
+    "            resized_spectr = cv2.resize(spectr, resize)\n",
+    "            img = np.asarray([resized_real, resized_mag, resized_spectr], dtype=np.float32)\n",
+    "            return img\n",
+    "        img = np.asarray([img2, img3, spectr], dtype=np.float32)\n",
+    "        return img\n",
+    "    except Exception as e:\n",
+    "        print(str(e))\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_binaries = '//192.168.11.63/data/DATASETS/Energomash/2400'\n",
+    "path_to_pictures = '//192.168.11.63/data/DATASETS/Energomash/2400_jpg'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6f226f86-5d72-4573-8af6-750128b70263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                          | 0/965 [00:00<?, ?it/s]C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\scipy\\signal\\_spectral_py.py:1936: RuntimeWarning: overflow encountered in multiply\n",
+      "  result = np.conjugate(result) * result\n",
+      "C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\scipy\\signal\\_spectral_py.py:1938: RuntimeWarning: invalid value encountered in multiply\n",
+      "  result *= scale\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████| 965/965 [28:11<00:00,  1.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  noise  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "size = (256,256)\n",
+    "if not os.path.exists(path_to_pictures):\n",
+    "    os.mkdir(path_to_pictures)\n",
+    "for subdir in os.listdir(path_to_binaries):\n",
+    "    filepath = path_to_binaries + '/' + subdir + '/'\n",
+    "    if not os.path.exists(path_to_pictures +'/' + subdir):\n",
+    "        os.mkdir(path_to_pictures + '/' + subdir)\n",
+    "    files = os.listdir(filepath)\n",
+    "    for file in tqdm(files):\n",
+    "        savepath = path_to_pictures +'/' + subdir + '/' + file + '.npy'\n",
+    "        savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.png' \n",
+    "        savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.png' \n",
+    "        savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.png'\n",
+    "        if not os.path.exists(savepath):\n",
+    "            img = sig2pic_with_spec(path_to_data=filepath, filename=file, specT=s, resize = size)\n",
+    "            gc.collect()\n",
+    "            try:\n",
+    "                \n",
+    "                plt.imshow(img[0])\n",
+    "                plt.savefig(savepath_real_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "                plt.imshow(img[1])\n",
+    "                plt.savefig(savepath_imag_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "\n",
+    "                plt.imshow(img[2])\n",
+    "                plt.savefig(savepath_spec_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "                np.save(savepath, img)\n",
+    "                \n",
+    "            except Exception:\n",
+    "                continue\n",
+    "    print('Dir: ', subdir , ' finished!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "106b1add",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/ImageDatasetCreate_spec_imag_real_915-Copy1.ipynb

@@ -0,0 +1,428 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<contextlib.ExitStack at 0x73285e4f6300>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import io\n",
+    "import cv2\n",
+    "import copy\n",
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "import torch.nn as nn\n",
+    "import torch\n",
+    "import torchvision\n",
+    "from torch.utils.data import Dataset\n",
+    "from torch import default_generator, randperm\n",
+    "from PIL import Image\n",
+    "#from torch._utils import _accumulate\n",
+    "import csv\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "from scipy import ndimage\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)\n",
+    "batch_size = 16\n",
+    "momentum=0.9\n",
+    "lr = 1e-3\n",
+    "import random\n",
+    "sub_sample = 0.5\n",
+    "import matplotlib\n",
+    "import gc\n",
+    "import torchsig.utils as u\n",
+    "import torchsig.transforms.transforms as T\n",
+    "matplotlib.use('Agg')\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['agg.path.chunksize'] = 256*256\n",
+    "plt.ioff()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4848b066-2e09-4c1c-b8fa-8e3fa84d907a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = T.Spectrogram(nperseg=256)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9267fbe1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sig2pic_with_spec(path_to_data, filename, specT=None, figsize=(16,16), dpi=16, resize = None):\n",
+    "    def standartize_signal(signal):\n",
+    "        mean = np.mean(signal)\n",
+    "        std = np.std(signal)\n",
+    "        standardized_signal = (signal - mean) / std\n",
+    "        return standardized_signal\n",
+    "    \n",
+    "    try:\n",
+    "        if specT is None:\n",
+    "            specT = T.Spectrogram(nperseg=256)\n",
+    "        with open(path_to_data + filename, 'rb') as file:\n",
+    "            tmp = np.frombuffer(file.read(), dtype=np.complex64)\n",
+    "        signal = tmp\n",
+    "        print(len(signal))\n",
+    "        spectr = np.array(specT(signal)['data']['samples'][:,:figsize[0] * dpi])\n",
+    "        mag = np.abs(signal)\n",
+    "        mag = standartize_signal(mag)\n",
+    "        real = signal.real\n",
+    "        real = standartize_signal(real)\n",
+    "\n",
+    "        fig2 = plt.figure(figsize = figsize)\n",
+    "        plt.axes(ylim=(-1, 1))\n",
+    "\n",
+    "        plt.plot(real, color='black')\n",
+    "        plt.gca().set_axis_off()\n",
+    "        plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
+    "        plt.margins(0,0)\n",
+    "        buf2 = io.BytesIO()\n",
+    "        fig2.savefig(buf2, format=\"png\", dpi=dpi)\n",
+    "        buf2.seek(0)\n",
+    "        img_arr2 = np.frombuffer(buf2.getvalue(), dtype=np.uint8)\n",
+    "        buf2.close()\n",
+    "        img2 = cv2.imdecode(img_arr2, 1)\n",
+    "        img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
+    "        plt.clf()\n",
+    "        plt.cla()\n",
+    "        plt.close()\n",
+    "        plt.close(fig2)\n",
+    "\n",
+    "        fig3 = plt.figure(figsize = figsize)\n",
+    "        plt.axes(ylim=(-1, 1))\n",
+    "\n",
+    "        plt.plot(mag, color='black')\n",
+    "        plt.gca().set_axis_off()\n",
+    "        plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
+    "        plt.margins(0,0)\n",
+    "        buf3 = io.BytesIO()\n",
+    "        fig3.savefig(buf3, format=\"png\", dpi=dpi)\n",
+    "        buf3.seek(0)\n",
+    "        img_arr3 = np.frombuffer(buf3.getvalue(), dtype=np.uint8)\n",
+    "        buf3.close()\n",
+    "        img3 = cv2.imdecode(img_arr3, 1)\n",
+    "        img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)\n",
+    "        plt.clf()\n",
+    "        plt.cla()\n",
+    "        plt.close()\n",
+    "        plt.close(fig3)\n",
+    "\n",
+    "        if resize != None:\n",
+    "            resized_real = cv2.resize(img2, resize)\n",
+    "            resized_mag = cv2.resize(img3, resize)\n",
+    "            resized_spectr = cv2.resize(spectr, resize)\n",
+    "            img = np.asarray([resized_real, resized_mag, resized_spectr], dtype=np.float32)\n",
+    "            return img\n",
+    "        img = np.asarray([img2, img3, spectr], dtype=np.float32)\n",
+    "        return img\n",
+    "    except Exception as e:\n",
+    "        print(str(e))\n",
+    "        return None\n",
+    "\n",
+    "def plot_signal_and_magnitude(path_to_data, filename, filename_signal):\n",
+    "    def remove_outliers(signal, threshold):\n",
+    "        filtered_signal = np.where(np.abs(signal) <= threshold, signal, np.nan)\n",
+    "        return np.nan_to_num(filtered_signal)\n",
+    "    \n",
+    "    def standartize_signal(signal):\n",
+    "        mean = np.mean(signal)\n",
+    "        std = np.std(signal)\n",
+    "        standardized_signal = (signal - mean) / std\n",
+    "        return standardized_signal\n",
+    "    \n",
+    "    with open(path_to_data + filename, 'rb') as file:\n",
+    "        signal = np.frombuffer(file.read(), dtype=np.complex64)\n",
+    "    print(max(np.real(signal)))\n",
+    "    print(signal[:100])\n",
+    "    plt.figure(figsize=(12, 6))\n",
+    "    plt.subplot(2, 1, 1)\n",
+    "    plt.plot(remove_outliers(standartize_signal(np.real(signal)),1)[10000:], label='Real Part')\n",
+    "    plt.plot(remove_outliers(standartize_signal(np.imag(signal)),1)[10000:], label='Imaginary Part')\n",
+    "    plt.title('QAM Signal')\n",
+    "    plt.legend()\n",
+    "    plt.subplot(2, 1, 2)\n",
+    "    plt.plot(np.abs(signal), label='Magnitude')\n",
+    "    plt.title('Magnitude of QAM Signal')\n",
+    "    plt.legend()\n",
+    "    plt.tight_layout()\n",
+    "    plt.savefig(filename_signal)\n",
+    "    plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_binaries = '/home/sibscience/Datasets/915_9K'\n",
+    "path_to_pictures = '/home/sibscience/Datasets/915_9K_jpg'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6a5f4c51",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '/home/sibscience/Datasets/915_9K_jpg'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m size = (\u001b[32m256\u001b[39m,\u001b[32m256\u001b[39m)\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os.path.exists(path_to_pictures):\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m     \u001b[43mos\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_to_pictures\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m subdir \u001b[38;5;129;01min\u001b[39;00m os.listdir(path_to_binaries):\n\u001b[32m      5\u001b[39m     filepath = path_to_binaries + \u001b[33m'\u001b[39m\u001b[33m/\u001b[39m\u001b[33m'\u001b[39m + subdir + \u001b[33m'\u001b[39m\u001b[33m/\u001b[39m\u001b[33m'\u001b[39m\n",
+      "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: '/home/sibscience/Datasets/915_9K_jpg'"
+     ]
+    }
+   ],
+   "source": [
+    "size = (256,256)\n",
+    "if not os.path.exists(path_to_pictures):\n",
+    "    os.mkdir(path_to_pictures)\n",
+    "for subdir in os.listdir(path_to_binaries):\n",
+    "    filepath = path_to_binaries + '/' + subdir + '/'\n",
+    "    if not os.path.exists(path_to_pictures +'/' + subdir):\n",
+    "        os.mkdir(path_to_pictures + '/' + subdir)\n",
+    "    files = os.listdir(filepath)\n",
+    "    for file in tqdm(files):\n",
+    "        savepath = path_to_pictures +'/' + subdir + '/' + file + '.npy'\n",
+    "        savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '.png' \n",
+    "        if not os.path.exists(savepath):\n",
+    "            img = plot_signal_and_magnitude(path_to_data=filepath, filename=file, filename_signal= savepath_real_png)\n",
+    "            gc.collect()\n",
+    "    print('Dir: ', subdir , ' finished!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f226f86-5d72-4573-8af6-750128b70263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r\n",
+      "  0%|                                                                                          | 0/565 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "800016\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r\n",
+      " 28%|█████████████████████▉                                                         | 157/565 [00:01<00:02, 145.94it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "800016\n",
+      "800016\n",
+      "800016\n",
+      "800016\n",
+      "800016\n",
+      "800016\n",
+      "800016\n",
+      "800016\n",
+      "800016\n",
+      "800016\n",
+      "800016\n",
+      "800016\n",
+      "800016\n",
+      "800016\n",
+      "800016\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r\n",
+      " 30%|████████████████████████▎                                                       | 172/565 [00:15<00:46,  8.38it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "800016\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 31%|████████████████████████▍                                                       | 173/565 [00:16<00:37, 10.59it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "800016\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[10], line 15\u001b[0m\n\u001b[0;32m     13\u001b[0m savepath_spec_png \u001b[38;5;241m=\u001b[39m path_to_pictures \u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m subdir \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m file \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_spec\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.png\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(savepath):\n\u001b[1;32m---> 15\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[43msig2pic_with_spec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_to_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilepath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mspecT\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresize\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     16\u001b[0m     gc\u001b[38;5;241m.\u001b[39mcollect()\n\u001b[0;32m     17\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
+      "Cell \u001b[1;32mIn[7], line 29\u001b[0m, in \u001b[0;36msig2pic_with_spec\u001b[1;34m(path_to_data, filename, specT, figsize, dpi, resize)\u001b[0m\n\u001b[0;32m     27\u001b[0m plt\u001b[38;5;241m.\u001b[39mmargins(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m     28\u001b[0m buf2 \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mBytesIO()\n\u001b[1;32m---> 29\u001b[0m \u001b[43mfig2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavefig\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuf2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpng\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     30\u001b[0m buf2\u001b[38;5;241m.\u001b[39mseek(\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m     31\u001b[0m img_arr2 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mfrombuffer(buf2\u001b[38;5;241m.\u001b[39mgetvalue(), dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39muint8)\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\figure.py:3390\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[1;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[0;32m   3388\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n\u001b[0;32m   3389\u001b[0m         _recursively_make_axes_transparent(stack, ax)\n\u001b[1;32m-> 3390\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\backend_bases.py:2193\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2189\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   2190\u001b[0m     \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[0;32m   2191\u001b[0m     \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[0;32m   2192\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[1;32m-> 2193\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mprint_method\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2194\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2195\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfacecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfacecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2196\u001b[0m \u001b[43m            \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2197\u001b[0m \u001b[43m            \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2198\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbbox_inches_restore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_bbox_inches_restore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2199\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2200\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m   2201\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\backend_bases.py:2043\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method.<locals>.<lambda>\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   2039\u001b[0m     optional_kws \u001b[38;5;241m=\u001b[39m {  \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[0;32m   2040\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   2041\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[0;32m   2042\u001b[0m     skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[1;32m-> 2043\u001b[0m     print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2044\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mskip\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m   2045\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:  \u001b[38;5;66;03m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[0;32m   2046\u001b[0m     print_method \u001b[38;5;241m=\u001b[39m meth\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:497\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[0;32m    450\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprint_png\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39m, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    451\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    452\u001b[0m \u001b[38;5;124;03m    Write the figure to a PNG file.\u001b[39;00m\n\u001b[0;32m    453\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    495\u001b[0m \u001b[38;5;124;03m        *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[0;32m    496\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 497\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_print_pil\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpng\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:445\u001b[0m, in \u001b[0;36mFigureCanvasAgg._print_pil\u001b[1;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[0;32m    440\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_print_pil\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, fmt, pil_kwargs, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    441\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    442\u001b[0m \u001b[38;5;124;03m    Draw the canvas, then save it using `.image.imsave` (to which\u001b[39;00m\n\u001b[0;32m    443\u001b[0m \u001b[38;5;124;03m    *pil_kwargs* and *metadata* are forwarded).\u001b[39;00m\n\u001b[0;32m    444\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 445\u001b[0m     \u001b[43mFigureCanvasAgg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    446\u001b[0m     mpl\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mimsave(\n\u001b[0;32m    447\u001b[0m         filename_or_obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer_rgba(), \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39mfmt, origin\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    448\u001b[0m         dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdpi, metadata\u001b[38;5;241m=\u001b[39mmetadata, pil_kwargs\u001b[38;5;241m=\u001b[39mpil_kwargs)\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:388\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    385\u001b[0m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[0;32m    386\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[0;32m    387\u001b[0m       \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[1;32m--> 388\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    389\u001b[0m     \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[0;32m    390\u001b[0m     \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[0;32m    391\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdraw()\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[0;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[0;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\figure.py:3154\u001b[0m, in \u001b[0;36mFigure.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   3151\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[0;32m   3153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m-> 3154\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3155\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3157\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[0;32m   3158\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[0;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[1;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3070\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   3067\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[0;32m   3068\u001b[0m     _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[1;32m-> 3070\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3071\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3073\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m   3074\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[0;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[1;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\lines.py:801\u001b[0m, in \u001b[0;36mLine2D.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    798\u001b[0m         gc\u001b[38;5;241m.\u001b[39mset_foreground(lc_rgba, isRGBA\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    800\u001b[0m         gc\u001b[38;5;241m.\u001b[39mset_dashes(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dash_pattern)\n\u001b[1;32m--> 801\u001b[0m         \u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maffine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrozen\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    802\u001b[0m         gc\u001b[38;5;241m.\u001b[39mrestore()\n\u001b[0;32m    804\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_marker \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_markersize \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:117\u001b[0m, in \u001b[0;36mRendererAgg.draw_path\u001b[1;34m(self, gc, path, transform, rgbFace)\u001b[0m\n\u001b[0;32m    115\u001b[0m p\u001b[38;5;241m.\u001b[39msimplify_threshold \u001b[38;5;241m=\u001b[39m path\u001b[38;5;241m.\u001b[39msimplify_threshold\n\u001b[0;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_renderer\u001b[38;5;241m.\u001b[39mdraw_path(gc, p, transform, rgbFace)\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOverflowError\u001b[39;00m:\n\u001b[0;32m    119\u001b[0m     msg \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    120\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExceeded cell block limit in Agg.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    121\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease reduce the value of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    127\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;241m.\u001b[39msimplify_threshold\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m on the input).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    128\u001b[0m     )\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "size = (256,256)\n",
+    "if not os.path.exists(path_to_pictures):\n",
+    "    os.mkdir(path_to_pictures)\n",
+    "for subdir in os.listdir(path_to_binaries):\n",
+    "    filepath = path_to_binaries + '/' + subdir + '/'\n",
+    "    if not os.path.exists(path_to_pictures +'/' + subdir):\n",
+    "        os.mkdir(path_to_pictures + '/' + subdir)\n",
+    "    files = os.listdir(filepath)\n",
+    "    for file in tqdm(files):\n",
+    "        savepath = path_to_pictures +'/' + subdir + '/' + file + '.npy'\n",
+    "        savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.png' \n",
+    "        savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.png' \n",
+    "        savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.png'\n",
+    "        if not os.path.exists(savepath):\n",
+    "            img = sig2pic_with_spec(path_to_data=filepath, filename=file, specT=s, resize = size)\n",
+    "            gc.collect()\n",
+    "            try:\n",
+    "                \n",
+    "                plt.imshow(img[0])\n",
+    "                plt.savefig(savepath_real_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "                plt.imshow(img[1])\n",
+    "                plt.savefig(savepath_imag_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "\n",
+    "                plt.imshow(img[2])\n",
+    "                plt.savefig(savepath_spec_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "                np.save(savepath, img)\n",
+    "                \n",
+    "            except Exception:\n",
+    "                continue\n",
+    "    print('Dir: ', subdir , ' finished!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58ff5fbd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f9ad366",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv-train",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/ImageDatasetCreate_spec_imag_real_915.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
    "metadata": {},
    "outputs": [
@@ -16,10 +16,10 @@
     {
      "data": {
       "text/plain": [
-       "<contextlib.ExitStack at 0x76adb5fa4260>"
+       "<contextlib.ExitStack at 0x7d183e4bd220>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "4848b066-2e09-4c1c-b8fa-8e3fa84d907a",
    "metadata": {},
    "outputs": [],
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "9267fbe1",
    "metadata": {},
    "outputs": [],
@@ -147,20 +147,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "selected_freq=915\n",
+    "selected_freq=750\n",
     "\n",
-    "path_to_binaries = f'/home/sibsci/dataset/drone/{selected_freq}'\n",
-    "path_to_pictures = f'/home/sibsci/dataset_img/drone/{selected_freq}_jpg'"
+    "path_to_binaries = f'/mnt/nvme1/dataset/{selected_freq}'\n",
+    "path_to_pictures = f'/mnt/nvme1/dataset_img/noise/{selected_freq}_jpg'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
    "metadata": {},
    "outputs": [],
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "id": "6f226f86-5d72-4573-8af6-750128b70263",
    "metadata": {},
    "outputs": [
@@ -178,14 +178,287 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
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+      "2026-03-25_16-59-25:   0%|          | 0/40 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-03-25_16-59-25: 100%|██████████| 40/40 [00:47<00:00,  1.18s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  2026-03-25_16-59-25  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-03-26_02-59-25: 100%|██████████| 40/40 [00:47<00:00,  1.18s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  2026-03-26_02-59-25  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-03-25_14-59-25: 100%|██████████| 40/40 [00:35<00:00,  1.11it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  2026-03-25_14-59-25  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-03-26_04-59-26: 100%|██████████| 40/40 [00:49<00:00,  1.23s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  2026-03-26_04-59-26  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-03-25_17-59-26: 100%|██████████| 40/40 [00:44<00:00,  1.12s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  2026-03-25_17-59-26  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  2026-03-25_20-59-26  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  2026-03-25_22-59-25  finished!\n"
+     ]
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+     "name": "stderr",
+     "output_type": "stream",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
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+      "Dir:  2026-03-25_14-56-12  finished!\n"
+     ]
+    },
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+     "name": "stderr",
+     "output_type": "stream",
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+     ]
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+     "name": "stdout",
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+     ]
+    },
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+     "name": "stderr",
+     "output_type": "stream",
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+     ]
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+     "name": "stdout",
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+      "Dir:  2026-03-25_19-59-25  finished!\n"
+     ]
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+     ]
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+     "name": "stdout",
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+      "Dir:  2026-03-26_05-59-25  finished!\n"
+     ]
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+     ]
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+    {
+     "name": "stdout",
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+     ]
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+     "output_type": "stream",
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+     ]
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+     "name": "stdout",
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+      "Dir:  2026-03-26_07-59-25  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+     ]
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+     "name": "stdout",
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+      "Dir:  2026-03-26_06-59-26  finished!\n"
+     ]
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+     ]
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+     ]
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+     ]
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+     "name": "stdout",
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+     ]
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+    {
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+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  2026-03-25_23-59-26  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  2026-03-25_21-59-26  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-03-25_18-59-25: 100%|██████████| 40/40 [00:47<00:00,  1.19s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  2026-03-25_18-59-25  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-03-26_03-59-26: 100%|██████████| 40/40 [00:47<00:00,  1.19s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dir:  2026-04-06_11-37-43  finished!\n"
+      "Dir:  2026-03-26_03-59-26  finished!\n"
      ]
     },
     {
@@ -200,7 +473,7 @@
     "size = (256,256)\n",
     "\n",
     "if not os.path.exists(path_to_pictures):\n",
-    "    os.makedirs(path_to_pictures,exist_ok=True)\n",
+    "    os.mkdir(path_to_pictures)\n",
     "\n",
     "for subdir in os.listdir(path_to_binaries):\n",
     "    filepath = path_to_binaries + '/' + subdir + '/'\n",
@@ -269,7 +542,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv-train",
+   "display_name": ".venv-train (3.12.3)",
    "language": "python",
    "name": "python3"
   },

train_scripts/ImageDatasetCreate_spec_imag_real_from_npy_to_jpg.ipynb

@@ -0,0 +1,222 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<contextlib.ExitStack at 0x249d580ff50>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import io\n",
+    "import cv2\n",
+    "import copy\n",
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "import torch.nn as nn\n",
+    "import torch\n",
+    "import torchvision\n",
+    "from torch.utils.data import Dataset\n",
+    "from torch import default_generator, randperm\n",
+    "from PIL import Image\n",
+    "#from torch._utils import _accumulate\n",
+    "import csv\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "from scipy import ndimage\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)\n",
+    "batch_size = 16\n",
+    "momentum=0.9\n",
+    "lr = 1e-3\n",
+    "import random\n",
+    "sub_sample = 0.5\n",
+    "import matplotlib\n",
+    "import gc\n",
+    "matplotlib.use('Agg')\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['agg.path.chunksize'] = 256*256\n",
+    "plt.ioff()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9267fbe1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sig2pic_with_spec(path_to_data, filename, figsize=(16,16), dpi=16, resize = None):\n",
+    "    try:\n",
+    "        array = np.load(path_to_data+filename)\n",
+    "\n",
+    "        if resize != None:\n",
+    "            resized_real = cv2.resize(array[0], resize)\n",
+    "            resized_mag = cv2.resize(array[1], resize)\n",
+    "            resized_spectr = cv2.resize(array[2], resize)\n",
+    "            img = np.asarray(array, dtype=np.float32)\n",
+    "            return img\n",
+    "        \n",
+    "        img = np.asarray(array, dtype=np.float32)\n",
+    "        return img\n",
+    "    \n",
+    "    except Exception as e:\n",
+    "        print(str(e))\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_binaries = 'C:/Users/snytk/Lerning_NN_for_work/datasets/2.4_learning'\n",
+    "path_to_pictures = 'C:/Users/snytk/Lerning_NN_for_work/datasets_jpg/2.4_jpg_learning'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f226f86-5d72-4573-8af6-750128b70263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████| 8751/8751 [1:01:54<00:00,  2.36it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  drone  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████| 11202/11202 [1:19:30<00:00,  2.35it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  noise  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "size = (256,256)\n",
+    "if not os.path.exists(path_to_pictures):\n",
+    "    os.mkdir(path_to_pictures)\n",
+    "for subdir in os.listdir(path_to_binaries):\n",
+    "    filepath = path_to_binaries + '/' + subdir + '/'\n",
+    "    if not os.path.exists(path_to_pictures +'/' + subdir):\n",
+    "        os.mkdir(path_to_pictures + '/' + subdir)\n",
+    "    files = os.listdir(filepath)\n",
+    "    for file in tqdm(files):\n",
+    "        savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.jpg' \n",
+    "        savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.jpg' \n",
+    "        savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.jpg'\n",
+    "        if not os.path.exists(savepath_real_png):\n",
+    "            img = sig2pic_with_spec(path_to_data=filepath, filename=file, resize = size)\n",
+    "            gc.collect()\n",
+    "            \n",
+    "            try:\n",
+    "                plt.imshow(img[0])\n",
+    "                plt.savefig(savepath_real_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "                plt.imshow(img[1])\n",
+    "                plt.savefig(savepath_imag_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "\n",
+    "                plt.imshow(img[2])\n",
+    "                plt.savefig(savepath_spec_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "            except Exception:\n",
+    "                continue\n",
+    "    print('Dir: ', subdir , ' finished!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cfbd309d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/ImageDatasetCreate_spec_imag_real_from_npy_to_jpg_1.2.ipynb

@@ -0,0 +1,360 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<contextlib.ExitStack at 0x20af0408250>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import io\n",
+    "import cv2\n",
+    "import copy\n",
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "import torch.nn as nn\n",
+    "import torch\n",
+    "import torchvision\n",
+    "from torch.utils.data import Dataset\n",
+    "from torch import default_generator, randperm\n",
+    "from PIL import Image\n",
+    "#from torch._utils import _accumulate\n",
+    "import csv\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "from scipy import ndimage\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)\n",
+    "batch_size = 16\n",
+    "momentum=0.9\n",
+    "lr = 1e-3\n",
+    "import random\n",
+    "sub_sample = 0.5\n",
+    "import matplotlib\n",
+    "import gc\n",
+    "matplotlib.use('Agg')\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['agg.path.chunksize'] = 256*256\n",
+    "plt.ioff()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9267fbe1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sig2pic_with_spec(path_to_data, filename, figsize=(16,16), dpi=16, resize = None):\n",
+    "    try:\n",
+    "        array = np.load(path_to_data+filename)\n",
+    "\n",
+    "        if resize != None:\n",
+    "            resized_real = cv2.resize(array[0], resize)\n",
+    "            resized_mag = cv2.resize(array[1], resize)\n",
+    "            resized_spectr = cv2.resize(array[2], resize)\n",
+    "            img = np.asarray(array, dtype=np.float32)\n",
+    "            return img\n",
+    "        \n",
+    "        img = np.asarray(array, dtype=np.float32)\n",
+    "        return img\n",
+    "    \n",
+    "    except Exception as e:\n",
+    "        print(str(e))\n",
+    "        return None\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "\n",
+    "def pre_func_ensemble(data=None, src ='', ind_inference=0):\n",
+    "    try:\n",
+    "        import matplotlib.pyplot as plt\n",
+    "        matplotlib.use('Agg')\n",
+    "        plt.ioff()\n",
+    "\n",
+    "        figsize = (16, 16)\n",
+    "        dpi = 16\n",
+    "\n",
+    "        signal = np.vectorize(complex)(data[0], data[1])\n",
+    "        #np.save(src + '_inference_2400_' + str(ind_inference) + '.npy', signal)\n",
+    "        spec = transform.Spectrogram(nperseg=256)\n",
+    "        spectr = np.array(spec(signal)[:,:figsize[0] * dpi])\n",
+    "        fig1 = plt.figure(figsize = figsize)\n",
+    "        plt.axes(ylim=(-1, 1))\n",
+    "        sigr = signal.real\n",
+    "        sigi = signal.imag\n",
+    "        \n",
+    "        plt.plot(sigr, color='black')\n",
+    "        plt.gca().set_axis_off()\n",
+    "        plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
+    "        plt.margins(0,0)\n",
+    "        buf1 = io.BytesIO()\n",
+    "        fig1.savefig(buf1, format=\"png\", dpi=dpi)\n",
+    "        buf1.seek(0)\n",
+    "        img_arr1 = np.frombuffer(buf1.getvalue(), dtype=np.uint8)\n",
+    "        buf1.close()\n",
+    "        img1 = cv2.imdecode(img_arr1, 1)\n",
+    "        img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n",
+    "        plt.clf()\n",
+    "        plt.cla()\n",
+    "        plt.close()\n",
+    "        plt.close(fig1)\n",
+    "\n",
+    "        fig2 = plt.figure(figsize = figsize)\n",
+    "        plt.axes(ylim=(-1, 1))\n",
+    "        sigr = signal.real\n",
+    "        sigi = signal.imag\n",
+    "        \n",
+    "        plt.plot(sigi, color='black')\n",
+    "        plt.gca().set_axis_off()\n",
+    "        plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
+    "        plt.margins(0,0)\n",
+    "        buf = io.BytesIO()\n",
+    "        fig2.savefig(buf, format=\"png\", dpi=dpi)\n",
+    "        buf.seek(0)\n",
+    "        img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)\n",
+    "        buf.close()\n",
+    "        img = cv2.imdecode(img_arr, 1)\n",
+    "        img2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
+    "        plt.clf()\n",
+    "        plt.cla()\n",
+    "        plt.close()\n",
+    "        plt.close(fig2)\n",
+    "\n",
+    "        img = np.array([img1, img2, spectr])\n",
+    " \n",
+    "        cv2.destroyAllWindows()\n",
+    "        del signal\n",
+    "        del spec\n",
+    "        del spectr\n",
+    "        del img1\n",
+    "        del img2\n",
+    "        del sigr\n",
+    "        del sigi\n",
+    "        del buf\n",
+    "        del buf1\n",
+    "        del img_arr\n",
+    "        del img_arr1\n",
+    "        cv2.destroyAllWindows()\n",
+    "        gc.collect()\n",
+    "\n",
+    "        print('Подготовка данных завершена')\n",
+    "        print()\n",
+    "        return img\n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(str(e))\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "def build_func_ensemble(file_model='', file_config='', num_classes=None):\n",
+    "    try:\n",
+    "        import matplotlib.pyplot as plt\n",
+    "        matplotlib.use('Agg')\n",
+    "        plt.ioff()\n",
+    "        torch.cuda.empty_cache()\n",
+    "        model1 = models.resnet18(pretrained=False)\n",
+    "        model2 = models.resnet50(pretrained=False)\n",
+    "        model3 = models.resnet101(pretrained=False)\n",
+    "\n",
+    "        num_classes = 2\n",
+    "\n",
+    "        model1.fc = nn.Linear(model1.fc.in_features, num_classes)\n",
+    "        model2.fc = nn.Linear(model2.fc.in_features, num_classes)\n",
+    "        model3.fc = nn.Linear(model3.fc.in_features, num_classes)\n",
+    "\n",
+    "        class Ensemble(nn.Module):\n",
+    "            def __init__(self, model1, model2, model3):\n",
+    "                super(Ensemble, self).__init__()\n",
+    "                self.model1 = model1\n",
+    "                self.model2 = model2\n",
+    "                self.model3 = model3\n",
+    "                self.fc = nn.Linear(3 * num_classes, num_classes)\n",
+    "\n",
+    "            def forward(self, x):\n",
+    "                x1 = self.model1(x)\n",
+    "                x2 = self.model2(x)\n",
+    "                x3 = self.model3(x)\n",
+    "                x = torch.cat((x1, x2, x3), dim=1)\n",
+    "                x = self.fc(x)\n",
+    "                return x\n",
+    "\n",
+    "        model = Ensemble(model1, model2, model3)\n",
+    "\n",
+    "        device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "        if device != 'cpu':\n",
+    "            model = model.to(device)\n",
+    "        model.load_state_dict(torch.load(file_model, map_location=device))\n",
+    "        model.eval()\n",
+    "\n",
+    "        cv2.destroyAllWindows()\n",
+    "        del model1\n",
+    "        del model2\n",
+    "        del model3\n",
+    "        gc.collect()\n",
+    "\n",
+    "        print('Инициализация модели завершена')\n",
+    "        print()\n",
+    "        return model\n",
+    "\n",
+    "    except Exception as exc:\n",
+    "        print(str(exc))\n",
+    "        return None\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_binaries = 'C:/Users/snytk/Lerning_NN_for_work/datasets/1.2_learning'\n",
+    "path_to_pictures = 'C:/Users/snytk/Lerning_NN_for_work/datasets_jpg/1.2_jpg_learning'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6f226f86-5d72-4573-8af6-750128b70263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████| 963/963 [06:36<00:00,  2.43it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  drone  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████| 1724/1724 [11:41<00:00,  2.46it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  noise  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "size = (256,256)\n",
+    "if not os.path.exists(path_to_pictures):\n",
+    "    os.mkdir(path_to_pictures)\n",
+    "for subdir in os.listdir(path_to_binaries):\n",
+    "    filepath = path_to_binaries + '/' + subdir + '/'\n",
+    "    if not os.path.exists(path_to_pictures +'/' + subdir):\n",
+    "        os.mkdir(path_to_pictures + '/' + subdir)\n",
+    "    files = os.listdir(filepath)\n",
+    "    for file in tqdm(files):\n",
+    "        savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.jpg' \n",
+    "        savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.jpg' \n",
+    "        savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.jpg'\n",
+    "        if not os.path.exists(savepath_real_png):\n",
+    "            img = sig2pic_with_spec(path_to_data=filepath, filename=file, resize = size)\n",
+    "            gc.collect()\n",
+    "            \n",
+    "            try:\n",
+    "                plt.imshow(img[0])\n",
+    "                plt.savefig(savepath_real_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "                plt.imshow(img[1])\n",
+    "                plt.savefig(savepath_imag_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "\n",
+    "                plt.imshow(img[2])\n",
+    "                plt.savefig(savepath_spec_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "            except Exception:\n",
+    "                continue\n",
+    "    print('Dir: ', subdir , ' finished!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "871d7ab6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/ImageDatasetCreate_spec_imag_real_from_npy_to_jpg_2.4.ipynb

@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<contextlib.ExitStack at 0x249d580ff50>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import io\n",
+    "import cv2\n",
+    "import copy\n",
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "import torch.nn as nn\n",
+    "import torch\n",
+    "import torchvision\n",
+    "from torch.utils.data import Dataset\n",
+    "from torch import default_generator, randperm\n",
+    "from PIL import Image\n",
+    "#from torch._utils import _accumulate\n",
+    "import csv\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "from scipy import ndimage\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)\n",
+    "batch_size = 16\n",
+    "momentum=0.9\n",
+    "lr = 1e-3\n",
+    "import random\n",
+    "sub_sample = 0.5\n",
+    "import matplotlib\n",
+    "import gc\n",
+    "matplotlib.use('Agg')\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['agg.path.chunksize'] = 256*256\n",
+    "plt.ioff()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9267fbe1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sig2pic_with_spec(path_to_data, filename, figsize=(16,16), dpi=16, resize = None):\n",
+    "    try:\n",
+    "        array = np.load(path_to_data+filename)\n",
+    "\n",
+    "        if resize != None:\n",
+    "            resized_real = cv2.resize(array[0], resize)\n",
+    "            resized_mag = cv2.resize(array[1], resize)\n",
+    "            resized_spectr = cv2.resize(array[2], resize)\n",
+    "            img = np.asarray(array, dtype=np.float32)\n",
+    "            return img\n",
+    "        \n",
+    "        img = np.asarray(array, dtype=np.float32)\n",
+    "        return img\n",
+    "    \n",
+    "    except Exception as e:\n",
+    "        print(str(e))\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_binaries = 'C:/Users/snytk/Lerning_NN_for_work/datasets/2.4_learning'\n",
+    "path_to_pictures = 'C:/Users/snytk/Lerning_NN_for_work/datasets_jpg/2.4_jpg_learning'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f226f86-5d72-4573-8af6-750128b70263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  2%|█▍                                                                           | 158/8751 [01:06<1:01:14,  2.34it/s]"
+     ]
+    }
+   ],
+   "source": [
+    "size = (256,256)\n",
+    "if not os.path.exists(path_to_pictures):\n",
+    "    os.mkdir(path_to_pictures)\n",
+    "for subdir in os.listdir(path_to_binaries):\n",
+    "    filepath = path_to_binaries + '/' + subdir + '/'\n",
+    "    if not os.path.exists(path_to_pictures +'/' + subdir):\n",
+    "        os.mkdir(path_to_pictures + '/' + subdir)\n",
+    "    files = os.listdir(filepath)\n",
+    "    for file in tqdm(files):\n",
+    "        savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.jpg' \n",
+    "        savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.jpg' \n",
+    "        savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.jpg'\n",
+    "        if not os.path.exists(savepath_real_png):\n",
+    "            img = sig2pic_with_spec(path_to_data=filepath, filename=file, resize = size)\n",
+    "            gc.collect()\n",
+    "            \n",
+    "            try:\n",
+    "                plt.imshow(img[0])\n",
+    "                plt.savefig(savepath_real_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "                plt.imshow(img[1])\n",
+    "                plt.savefig(savepath_imag_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "\n",
+    "                plt.imshow(img[2])\n",
+    "                plt.savefig(savepath_spec_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "            except Exception:\n",
+    "                continue\n",
+    "    print('Dir: ', subdir , ' finished!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "871d7ab6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/ImageDatasetCreate_spec_imag_real_from_npy_to_jpg_2_4_Copy1.ipynb

@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<contextlib.ExitStack at 0x249d580ff50>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import io\n",
+    "import cv2\n",
+    "import copy\n",
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "import torch.nn as nn\n",
+    "import torch\n",
+    "import torchvision\n",
+    "from torch.utils.data import Dataset\n",
+    "from torch import default_generator, randperm\n",
+    "from PIL import Image\n",
+    "#from torch._utils import _accumulate\n",
+    "import csv\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "from scipy import ndimage\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)\n",
+    "batch_size = 16\n",
+    "momentum=0.9\n",
+    "lr = 1e-3\n",
+    "import random\n",
+    "sub_sample = 0.5\n",
+    "import matplotlib\n",
+    "import gc\n",
+    "matplotlib.use('Agg')\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['agg.path.chunksize'] = 256*256\n",
+    "plt.ioff()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9267fbe1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sig2pic_with_spec(path_to_data, filename, figsize=(16,16), dpi=16, resize = None):\n",
+    "    try:\n",
+    "        array = np.load(path_to_data+filename)\n",
+    "\n",
+    "        if resize != None:\n",
+    "            resized_real = cv2.resize(array[0], resize)\n",
+    "            resized_mag = cv2.resize(array[1], resize)\n",
+    "            resized_spectr = cv2.resize(array[2], resize)\n",
+    "            img = np.asarray(array, dtype=np.float32)\n",
+    "            return img\n",
+    "        \n",
+    "        img = np.asarray(array, dtype=np.float32)\n",
+    "        return img\n",
+    "    \n",
+    "    except Exception as e:\n",
+    "        print(str(e))\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_binaries = 'C:/Users/snytk/Lerning_NN_for_work/datasets/2.4_learning'\n",
+    "path_to_pictures = 'C:/Users/snytk/Lerning_NN_for_work/datasets_jpg/2.4_jpg_learning'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f226f86-5d72-4573-8af6-750128b70263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  2%|█▍                                                                           | 158/8751 [01:06<1:01:14,  2.34it/s]"
+     ]
+    }
+   ],
+   "source": [
+    "size = (256,256)\n",
+    "if not os.path.exists(path_to_pictures):\n",
+    "    os.mkdir(path_to_pictures)\n",
+    "for subdir in os.listdir(path_to_binaries):\n",
+    "    filepath = path_to_binaries + '/' + subdir + '/'\n",
+    "    if not os.path.exists(path_to_pictures +'/' + subdir):\n",
+    "        os.mkdir(path_to_pictures + '/' + subdir)\n",
+    "    files = os.listdir(filepath)\n",
+    "    for file in tqdm(files):\n",
+    "        savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.jpg' \n",
+    "        savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.jpg' \n",
+    "        savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.jpg'\n",
+    "        if not os.path.exists(savepath_real_png):\n",
+    "            img = sig2pic_with_spec(path_to_data=filepath, filename=file, resize = size)\n",
+    "            gc.collect()\n",
+    "            \n",
+    "            try:\n",
+    "                plt.imshow(img[0])\n",
+    "                plt.savefig(savepath_real_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "                plt.imshow(img[1])\n",
+    "                plt.savefig(savepath_imag_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "\n",
+    "                plt.imshow(img[2])\n",
+    "                plt.savefig(savepath_spec_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "            except Exception:\n",
+    "                continue\n",
+    "    print('Dir: ', subdir , ' finished!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "871d7ab6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/ImageDatasetCreate_spec_imag_real_from_npy_to_jpg_915.ipynb

@@ -0,0 +1,236 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n",
+      "['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<contextlib.ExitStack at 0x27e4cbae550>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import io\n",
+    "import cv2\n",
+    "import copy\n",
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "import torch.nn as nn\n",
+    "import torch\n",
+    "import torchvision\n",
+    "from torch.utils.data import Dataset\n",
+    "from torch import default_generator, randperm\n",
+    "from PIL import Image\n",
+    "#from torch._utils import _accumulate\n",
+    "import csv\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "from scipy import ndimage\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)\n",
+    "batch_size = 16\n",
+    "momentum=0.9\n",
+    "lr = 1e-3\n",
+    "import random\n",
+    "sub_sample = 0.5\n",
+    "import matplotlib\n",
+    "import gc\n",
+    "matplotlib.use('Agg')\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['agg.path.chunksize'] = 256*256\n",
+    "#plt.style.use('mplstyle')\n",
+    "plt.style.use('ggplot')\n",
+    "plt.grid(None)\n",
+    "plt.rcParams[\"axes.grid\"] = False\n",
+    "print(plt.style.available)\n",
+    "plt.ioff()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9267fbe1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sig2pic_with_spec(path_to_data, filename, figsize=(16,16), dpi=16, resize = None):\n",
+    "    try:\n",
+    "        array = np.load(path_to_data+filename)\n",
+    "\n",
+    "        if resize != None:\n",
+    "            resized_real = cv2.resize(array[0], resize)\n",
+    "            resized_mag = cv2.resize(array[1], resize)\n",
+    "            resized_spectr = cv2.resize(array[2], resize)\n",
+    "            img = np.asarray(array, dtype=np.float32)\n",
+    "            return img\n",
+    "        \n",
+    "        img = np.asarray(array, dtype=np.float32)\n",
+    "        return img\n",
+    "    \n",
+    "    except Exception as e:\n",
+    "        print(str(e))\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_binaries = 'C:/Users/snytk/Lerning_NN_for_work/datasets/915_learning'\n",
+    "path_to_pictures = 'C:/Users/snytk/Lerning_NN_for_work/datasets_jpg/915_jpg_learning'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6f226f86-5d72-4573-8af6-750128b70263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████| 856/856 [10:50<00:00,  1.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  drone  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████| 627/627 [08:40<00:00,  1.20it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  noise  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "size = (256,256)\n",
+    "if not os.path.exists(path_to_pictures):\n",
+    "    os.mkdir(path_to_pictures)\n",
+    "for subdir in os.listdir(path_to_binaries):\n",
+    "    filepath = path_to_binaries + '/' + subdir + '/'\n",
+    "    if not os.path.exists(path_to_pictures +'/' + subdir):\n",
+    "        os.mkdir(path_to_pictures + '/' + subdir)\n",
+    "    files = os.listdir(filepath)\n",
+    "    for file in tqdm(files):\n",
+    "        savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.jpg' \n",
+    "        savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.jpg' \n",
+    "        savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.jpg'\n",
+    "        if not os.path.exists(savepath_real_png):\n",
+    "            img = sig2pic_with_spec(path_to_data=filepath, filename=file, resize = size)\n",
+    "            gc.collect()\n",
+    "            \n",
+    "            try:\n",
+    "                plt.imshow(img[0])\n",
+    "                plt.savefig(savepath_real_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "                plt.imshow(img[1])\n",
+    "                plt.savefig(savepath_imag_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "\n",
+    "                plt.imshow(img[2])\n",
+    "                plt.savefig(savepath_spec_png)\n",
+    "                plt.clf()\n",
+    "                plt.cla()\n",
+    "                plt.close()\n",
+    "                \n",
+    "            except Exception:\n",
+    "                continue\n",
+    "    print('Dir: ', subdir , ' finished!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "871d7ab6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e080bb07",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/Jetson_пароли.txt

@@ -0,0 +1,7 @@
+Для ящика на выставки:
+aleksandr@192.168.3.85 19751975
+aleksandr@192.168.3.86 19751975
+
+Для ящика на Липецк:
+aleksandr@192.168.3.85 19751975
+aleksandr@192.168.3.86 19751975

train_scripts/PTH_to_PT.ipynb

@@ -0,0 +1,55 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a89c0273",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torchvision\n",
+    "\n",
+    "def convert_pth_to_pt(pth_path, pt_path, model_class):\n",
+    "    state_dict = torch.load(pth_path)\n",
+    "    model = model_class()\n",
+    "    model.load_state_dict(state_dict)\n",
+    "    torch.save(model, pt_path)\n",
+    "    print(f'Model saved to {pt_path}')\n",
+    "\n",
+    "class ModelClass(torch.nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(ModelClass, self).__init__()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        pass\n",
+    "\n",
+    "pth_path = 'model.pth'\n",
+    "pt_path = 'model.pt'\n",
+    "\n",
+    "convert_pth_to_pt(pth_path, pt_path, ModelClass)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/Training_models.ipynb

@@ -0,0 +1,463 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5a13ad6b-56c9-4381-b376-1765f6dd7553",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Импортирование библиотек"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7311cb4a-5bf3-4268-b431-43eea10e9ed6",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    },
+    {
+     "ename": "error",
+     "evalue": "OpenCV(4.10.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\highgui\\src\\window.cpp:1295: error: (-2:Unspecified error) The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Cocoa support. If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script in function 'cvDestroyAllWindows'\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 37\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[38;5;28mprint\u001b[39m(device)\n\u001b[0;32m     36\u001b[0m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mempty_cache()\n\u001b[1;32m---> 37\u001b[0m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroyAllWindows\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     38\u001b[0m gc\u001b[38;5;241m.\u001b[39mcollect()\n",
+      "\u001b[1;31merror\u001b[0m: OpenCV(4.10.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\highgui\\src\\window.cpp:1295: error: (-2:Unspecified error) The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Cocoa support. If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script in function 'cvDestroyAllWindows'\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from torch import default_generator, randperm\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "import torchvision.transforms as transforms\n",
+    "from torchvision.io import read_image\n",
+    "from importlib import import_module\n",
+    "import matplotlib.pyplot as plt\n",
+    "from torchvision import models\n",
+    "import torch, torchvision\n",
+    "from pathlib import Path\n",
+    "from PIL import Image\n",
+    "import torch.nn as nn\n",
+    "from tqdm import tqdm\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "import os, shutil\n",
+    "import mlconfig\n",
+    "import random\n",
+    "import shutil\n",
+    "import timeit\n",
+    "import copy\n",
+    "import time\n",
+    "import cv2\n",
+    "import csv\n",
+    "import sys\n",
+    "import io\n",
+    "import gc\n",
+    "\n",
+    "plt.rcParams[\"savefig.bbox\"] = 'tight'\n",
+    "torch.manual_seed(1)\n",
+    "#matplotlib.use('Agg')\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "print(device)\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "384de097-82c6-41f5-bda9-b2f54bc99593",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Подготовка и обучение детектирование"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "46e4dc99-6994-4fee-a32e-f3983bd991bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prepare_and_learning_detection(num_classes, num_samples, path_dataset, model_name, config_name, model):\n",
+    "    num_samples_per_class = num_samples // num_classes\n",
+    "\n",
+    "    #----------Создаём папку для сохранения результатов обучения--------------\n",
+    "    \n",
+    "    ind = 1\n",
+    "    while True:\n",
+    "        if os.path.exists(\"models/\" + model_name + str(ind)):\n",
+    "            ind += 1\n",
+    "        else:\n",
+    "            os.mkdir(\"models/\" + model_name + str(ind))\n",
+    "            path_res = \"models/\" + model_name + str(ind) + '/'\n",
+    "            break\n",
+    "    \n",
+    "    #----------Создаём файл dataset.csv для обучения--------------\n",
+    "    \n",
+    "    pd_columns = ['file_name']\n",
+    "    df = pd.DataFrame(columns=pd_columns)\n",
+    "    \n",
+    "    subdirs = os.listdir(path_dataset)\n",
+    "    for subdir in subdirs:\n",
+    "        files = os.listdir(path_dataset + subdir + '/')\n",
+    "        num_samples_per_class = min(num_samples_per_class, len(files))\n",
+    "    for subdir in subdirs:\n",
+    "        files = os.listdir(path_dataset + subdir + '/')\n",
+    "        random.shuffle(files)\n",
+    "        files_to_process = files[:num_samples_per_class]\n",
+    "        for file in files_to_process:\n",
+    "            row = pd.DataFrame({pd_columns[0]: [str(path_dataset + subdir + '/' + file)]})\n",
+    "            df = pd.concat([df, row], ignore_index=True)\n",
+    "    \n",
+    "    df.to_csv(path_res + 'dataset.csv', index=False)\n",
+    "    \n",
+    "    #----------Импортируем параметры для обучения--------------\n",
+    "    \n",
+    "    def load_function(attr):\n",
+    "        module_, func = attr.rsplit('.', maxsplit=1)\n",
+    "        return getattr(import_module(module_), func)\n",
+    "    \n",
+    "    config = mlconfig.load('config_' + config_name + '.yaml')\n",
+    "    \n",
+    "    #----------Создаём класс датасета--------------\n",
+    "    \n",
+    "    class MyDataset(Dataset):\n",
+    "        def __init__(self, path_dataset, csv_file):\n",
+    "            data=[]\n",
+    "            with open(path_dataset + csv_file, newline='') as csvfile:\n",
+    "                reader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n",
+    "                for row in list(reader)[1:]:\n",
+    "                    row = str(row)\n",
+    "                    data.append(row[2: len(row)-2])\n",
+    "            self.sig_filenames = data\n",
+    "            self.path_dataset = path_dataset\n",
+    "    \n",
+    "        def __len__(self):\n",
+    "            return len(self.sig_filenames)\n",
+    "    \n",
+    "        def __getitem__(self, idx):\n",
+    "            data_file = np.asarray(np.load(self.sig_filenames[idx], 'r+'), dtype=np.float32)\n",
+    "            if 'drone' in list(self.sig_filenames[idx].split('/')):\n",
+    "                label = torch.tensor(0)\n",
+    "            if 'noise' in list(self.sig_filenames[idx].split('/')):\n",
+    "                label = torch.tensor(1)\n",
+    "            return data_file, label\n",
+    "    \n",
+    "    #----------Создаём датасет--------------\n",
+    "    \n",
+    "    dataset = MyDataset(path_dataset=path_res, csv_file='dataset.csv')\n",
+    "    train_set, valid_set = torch.utils.data.random_split(dataset, [0.7, 0.3], generator=torch.Generator().manual_seed(42))\n",
+    "    batch_size = config.batch_size\n",
+    "    train_dataloader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "    valid_dataloader = torch.utils.data.DataLoader(valid_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "    \n",
+    "    dataloaders = {}\n",
+    "    dataloaders['train'] = train_dataloader\n",
+    "    dataloaders['val'] = valid_dataloader\n",
+    "    dataset_sizes = {}\n",
+    "    dataset_sizes['train'] = len(train_set)\n",
+    "    dataset_sizes['val'] = len(valid_set)\n",
+    "\n",
+    "    #----------Обучаем модель--------------\n",
+    "\n",
+    "    val_loss = []\n",
+    "    val_acc = []\n",
+    "    train_loss = []\n",
+    "    train_acc = []\n",
+    "    epochs = config.epoch\n",
+    "    \n",
+    "    best_acc = 0.0\n",
+    "    best_model = copy.deepcopy(model.state_dict())\n",
+    "    limit = config.limit\n",
+    "    epoch_limit = epochs\n",
+    "    \n",
+    "    start = timeit.default_timer()\n",
+    "    for epoch in range(1, epochs+1):\n",
+    "        print(f\"Epoch : {epoch}\\n\")\n",
+    "        dataloader = None\n",
+    "    \n",
+    "        for phase in ['train', 'val']:\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "    \n",
+    "            for (img, label) in tqdm(dataloaders[phase]):\n",
+    "                img, label = img.to(device), label.to(device)\n",
+    "                optimizer.zero_grad()\n",
+    "    \n",
+    "                with torch.set_grad_enabled(phase == 'train'):\n",
+    "                    output = model(img)\n",
+    "                    _, pred = torch.max(output.data, 1)\n",
+    "                    loss = criterion(output, label)\n",
+    "                    if phase=='train' :\n",
+    "                        loss.backward()\n",
+    "                        optimizer.step()\n",
+    "                \n",
+    "                running_loss += loss.item() * img.size(0)\n",
+    "                running_corrects += torch.sum(pred == label.data)\n",
+    "    \n",
+    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
+    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
+    "                \n",
+    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n",
+    "                    \n",
+    "            if phase=='train' :\n",
+    "                train_loss.append(epoch_loss)\n",
+    "                train_acc.append(epoch_acc)\n",
+    "            else :\n",
+    "                val_loss.append(epoch_loss)\n",
+    "                val_acc.append(epoch_acc)\n",
+    "                if val_acc[-1] > best_acc :\n",
+    "                    ind_limit = 0\n",
+    "                    best_acc = val_acc[-1]\n",
+    "                    best_model = copy.deepcopy(model.state_dict())\n",
+    "                    torch.save(best_model, path_res + model_name + '.pth')\n",
+    "                else:\n",
+    "                    ind_limit += 1\n",
+    "                    \n",
+    "                if ind_limit >= limit:\n",
+    "                    break\n",
+    "                \n",
+    "        if ind_limit >= limit:\n",
+    "            epoch_limit = epoch\n",
+    "            break\n",
+    "            \n",
+    "        print()\n",
+    "    \n",
+    "    end = timeit.default_timer()\n",
+    "    print(f\"Total time elapsed = {end - start} seconds\")\n",
+    "    epoch_limit += 1\n",
+    "    \n",
+    "    #----------Вывод графиков и сохранение результатов обучения--------------\n",
+    "    \n",
+    "    train_acc = np.asarray(list(map(lambda x: x.item(), train_acc)))\n",
+    "    val_acc = np.asarray(list(map(lambda x: x.item(), val_acc)))\n",
+    "    \n",
+    "    np.save(path_res+'train_acc.npy', train_acc)\n",
+    "    np.save(path_res+'val_acc.npy', val_acc)\n",
+    "    np.save(path_res+'train_loss.npy', train_loss)\n",
+    "    np.save(path_res+'val_loss.npy', val_loss)\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    plt.plot(range(1,epoch_limit), train_loss, color='blue')\n",
+    "    plt.plot(range(1,epoch_limit), val_loss, color='red')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Loss')\n",
+    "    plt.title('Loss Curve')\n",
+    "    plt.legend(['Train Loss', 'Validation Loss'])\n",
+    "    plt.show()\n",
+    "    plt.clf()\n",
+    "    plt.cla()\n",
+    "    plt.close()\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    plt.plot(range(1,epoch_limit), train_acc, color='blue')\n",
+    "    plt.plot(range(1,epoch_limit), val_acc, color='red')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Accuracy')\n",
+    "    plt.title('Accuracy Curve')\n",
+    "    plt.legend(['Train Accuracy', 'Validation Accuracy'])\n",
+    "    plt.show()\n",
+    "    \n",
+    "    plt.clf()\n",
+    "    plt.cla()\n",
+    "    plt.close()\n",
+    "    torch.cuda.empty_cache()\n",
+    "    cv2.destroyAllWindows()\n",
+    "    del model\n",
+    "    gc.collect()\n",
+    "\n",
+    "    return path_res, model_name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93c136ee",
+   "metadata": {},
+   "source": [
+    "### Ensemble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52e8d4c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch : 1\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████| 437/437 [23:49<00:00,  3.27s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train Loss: 0.0884 Acc: 0.9634\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████| 187/187 [09:15<00:00,  2.97s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "val Loss: 0.0342 Acc: 0.9873\n",
+      "\n",
+      "Epoch : 2\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 84%|██████████████████████████████████████████████████████████████████▊             | 365/437 [19:28<04:18,  3.59s/it]"
+     ]
+    }
+   ],
+   "source": [
+    "#----------Инициализируем модель и параметры обучения--------------\n",
+    "\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "gc.collect()\n",
+    "\n",
+    "num_classes = 3\n",
+    "config_name = \"ensemble\"\n",
+    "    \n",
+    "def load_function(attr):\n",
+    "    module_, func = attr.rsplit('.', maxsplit=1)\n",
+    "    return getattr(import_module(module_), func)\n",
+    "    \n",
+    "config = mlconfig.load('config_' + config_name + '.yaml')\n",
+    "\n",
+    "model1 = models.resnet18(pretrained=False)\n",
+    "model2 = models.resnet50(pretrained=False)\n",
+    "model3 = models.resnet101(pretrained=False)\n",
+    "\n",
+    "num_classes = 2\n",
+    "\n",
+    "model1.fc = nn.Linear(model1.fc.in_features, num_classes)\n",
+    "model2.fc = nn.Linear(model2.fc.in_features, num_classes)\n",
+    "model3.fc = nn.Linear(model3.fc.in_features, num_classes)\n",
+    "\n",
+    "class Ensemble(nn.Module):\n",
+    "    def __init__(self, model1, model2, model3):\n",
+    "        super(Ensemble, self).__init__()\n",
+    "        self.model1 = model1\n",
+    "        self.model2 = model2\n",
+    "        self.model3 = model3\n",
+    "        self.fc = nn.Linear(3 * num_classes, num_classes)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x1 = self.model1(x)\n",
+    "        x2 = self.model2(x)\n",
+    "        x3 = self.model3(x)\n",
+    "        x = torch.cat((x1, x2, x3), dim=1)\n",
+    "        x = self.fc(x)\n",
+    "        return x\n",
+    "\n",
+    "model = Ensemble(model1, model2, model3)\n",
+    "\n",
+    "optimizer = load_function(config.optimizer.name)(model.parameters(), lr=config.optimizer.lr)\n",
+    "criterion = load_function(config.loss_function.name)()\n",
+    "scheduler = load_function(config.scheduler.name)(optimizer, step_size=config.scheduler.step_size, gamma=config.scheduler.gamma)\n",
+    "\n",
+    "if device != 'cpu':\n",
+    "    model = model.to(device)\n",
+    "\n",
+    "#----------Создания датасета и обучение модели--------------\n",
+    "\n",
+    "path_res, model_name = prepare_and_learning_detection(num_classes = num_classes, num_samples = 10000, path_dataset = \"//192.168.1.64/data/DATASETS/2.4/2.4_learning/\", \n",
+    "                     model_name = config_name+\"_2.4_\", config_name = config_name, model=model)\n",
+    "\n",
+    "\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "del model\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57d18676",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Отсутствует",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/Training_models_2.4-Copy1.ipynb

@@ -0,0 +1,465 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5a13ad6b-56c9-4381-b376-1765f6dd7553",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Импортирование библиотек"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7311cb4a-5bf3-4268-b431-43eea10e9ed6",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from torch import default_generator, randperm\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "import torchvision.transforms as transforms\n",
+    "from torchvision.io import read_image\n",
+    "from importlib import import_module\n",
+    "import matplotlib.pyplot as plt\n",
+    "from torchvision import models\n",
+    "import torch, torchvision\n",
+    "from pathlib import Path\n",
+    "from PIL import Image\n",
+    "import torch.nn as nn\n",
+    "from tqdm import tqdm\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "import os, shutil\n",
+    "import mlconfig\n",
+    "import random\n",
+    "import shutil\n",
+    "import timeit\n",
+    "import copy\n",
+    "import time\n",
+    "import cv2\n",
+    "import csv\n",
+    "import sys\n",
+    "import io\n",
+    "import gc\n",
+    "\n",
+    "plt.rcParams[\"savefig.bbox\"] = 'tight'\n",
+    "torch.manual_seed(1)\n",
+    "#matplotlib.use('Agg')\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "print(device)\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "384de097-82c6-41f5-bda9-b2f54bc99593",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Подготовка и обучение детектирование"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "46e4dc99-6994-4fee-a32e-f3983bd991bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prepare_and_learning_detection(num_classes, num_samples, path_dataset, model_name, config_name, model):\n",
+    "    num_samples_per_class = num_samples // num_classes\n",
+    "\n",
+    "    #----------Создаём папку для сохранения результатов обучения--------------\n",
+    "    \n",
+    "    ind = 1\n",
+    "    while True:\n",
+    "        if os.path.exists(\"models/\" + model_name + str(ind)):\n",
+    "            ind += 1\n",
+    "        else:\n",
+    "            os.mkdir(\"models/\" + model_name + str(ind))\n",
+    "            path_res = \"models/\" + model_name + str(ind) + '/'\n",
+    "            break\n",
+    "    \n",
+    "    #----------Создаём файл dataset.csv для обучения--------------\n",
+    "    \n",
+    "    pd_columns = ['file_name']\n",
+    "    df = pd.DataFrame(columns=pd_columns)\n",
+    "    \n",
+    "    subdirs = os.listdir(path_dataset)\n",
+    "    for subdir in subdirs:\n",
+    "        files = os.listdir(path_dataset + subdir + '/')\n",
+    "        num_samples_per_class = min(num_samples_per_class, len(files))\n",
+    "    for subdir in subdirs:\n",
+    "        files = os.listdir(path_dataset + subdir + '/')\n",
+    "        random.shuffle(files)\n",
+    "        files_to_process = files[:num_samples_per_class]\n",
+    "        for file in files_to_process:\n",
+    "            row = pd.DataFrame({pd_columns[0]: [str(path_dataset + subdir + '/' + file)]})\n",
+    "            df = pd.concat([df, row], ignore_index=True)\n",
+    "    \n",
+    "    df.to_csv(path_res + 'dataset.csv', index=False)\n",
+    "    \n",
+    "    #----------Импортируем параметры для обучения--------------\n",
+    "    \n",
+    "    def load_function(attr):\n",
+    "        module_, func = attr.rsplit('.', maxsplit=1)\n",
+    "        return getattr(import_module(module_), func)\n",
+    "    \n",
+    "    config = mlconfig.load('config_' + config_name + '.yaml')\n",
+    "    \n",
+    "    #----------Создаём класс датасета--------------\n",
+    "    \n",
+    "    class MyDataset(Dataset):\n",
+    "        def __init__(self, path_dataset, csv_file):\n",
+    "            data=[]\n",
+    "            with open(path_dataset + csv_file, newline='') as csvfile:\n",
+    "                reader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n",
+    "                for row in list(reader)[1:]:\n",
+    "                    row = str(row)\n",
+    "                    data.append(row[2: len(row)-2])\n",
+    "            self.sig_filenames = data\n",
+    "            self.path_dataset = path_dataset\n",
+    "    \n",
+    "        def __len__(self):\n",
+    "            return len(self.sig_filenames)\n",
+    "    \n",
+    "        def __getitem__(self, idx):\n",
+    "            image_real = np.asarray(cv2.split(cv2.imread(self.sig_filenames[idx][:-8]+'real.jpg')), dtype=np.float32)\n",
+    "            if 'drone' in list(self.sig_filenames[idx].split('/')):\n",
+    "                label = torch.tensor(0)\n",
+    "            if 'noise' in list(self.sig_filenames[idx].split('/')):\n",
+    "                label = torch.tensor(1)\n",
+    "            return image_real, label\n",
+    "    \n",
+    "    #----------Создаём датасет--------------\n",
+    "    \n",
+    "    dataset = MyDataset(path_dataset=path_res, csv_file='dataset.csv')\n",
+    "    train_set, valid_set = torch.utils.data.random_split(dataset, [0.7, 0.3], generator=torch.Generator().manual_seed(42))\n",
+    "    batch_size = config.batch_size\n",
+    "    train_dataloader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "    valid_dataloader = torch.utils.data.DataLoader(valid_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "    \n",
+    "    dataloaders = {}\n",
+    "    dataloaders['train'] = train_dataloader\n",
+    "    dataloaders['val'] = valid_dataloader\n",
+    "    dataset_sizes = {}\n",
+    "    dataset_sizes['train'] = len(train_set)\n",
+    "    dataset_sizes['val'] = len(valid_set)\n",
+    "\n",
+    "    #----------Обучаем модель--------------\n",
+    "\n",
+    "    val_loss = []\n",
+    "    val_acc = []\n",
+    "    train_loss = []\n",
+    "    train_acc = []\n",
+    "    epochs = config.epoch\n",
+    "    \n",
+    "    best_acc = 0.0\n",
+    "    best_model = copy.deepcopy(model.state_dict())\n",
+    "    limit = config.limit\n",
+    "    epoch_limit = epochs\n",
+    "    \n",
+    "    start = timeit.default_timer()\n",
+    "    for epoch in range(1, epochs+1):\n",
+    "        print(f\"Epoch : {epoch}\\n\")\n",
+    "        dataloader = None\n",
+    "    \n",
+    "        for phase in ['train', 'val']:\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "    \n",
+    "            for (img, label) in tqdm(dataloaders[phase]):\n",
+    "                img, label = img.to(device), label.to(device)\n",
+    "                optimizer.zero_grad()\n",
+    "    \n",
+    "                with torch.set_grad_enabled(phase == 'train'):\n",
+    "                    output = model(img)\n",
+    "                    _, pred = torch.max(output.data, 1)\n",
+    "                    loss = criterion(output, label)\n",
+    "                    if phase=='train' :\n",
+    "                        loss.backward()\n",
+    "                        optimizer.step()\n",
+    "                \n",
+    "                running_loss += loss.item() * img.size(0)\n",
+    "                running_corrects += torch.sum(pred == label.data)\n",
+    "    \n",
+    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
+    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
+    "                \n",
+    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n",
+    "                    \n",
+    "            if phase=='train' :\n",
+    "                train_loss.append(epoch_loss)\n",
+    "                train_acc.append(epoch_acc)\n",
+    "            else :\n",
+    "                val_loss.append(epoch_loss)\n",
+    "                val_acc.append(epoch_acc)\n",
+    "                if val_acc[-1] > best_acc :\n",
+    "                    ind_limit = 0\n",
+    "                    best_acc = val_acc[-1]\n",
+    "                    best_model = copy.deepcopy(model.state_dict())\n",
+    "                    torch.save(best_model, path_res + model_name + '.pth')\n",
+    "                else:\n",
+    "                    ind_limit += 1\n",
+    "                    \n",
+    "                if ind_limit >= limit:\n",
+    "                    break\n",
+    "                \n",
+    "        if ind_limit >= limit:\n",
+    "            epoch_limit = epoch\n",
+    "            break\n",
+    "            \n",
+    "        print()\n",
+    "    \n",
+    "    end = timeit.default_timer()\n",
+    "    print(f\"Total time elapsed = {end - start} seconds\")\n",
+    "    epoch_limit += 1\n",
+    "    \n",
+    "    #----------Вывод графиков и сохранение результатов обучения--------------\n",
+    "    \n",
+    "    train_acc = np.asarray(list(map(lambda x: x.item(), train_acc)))\n",
+    "    val_acc = np.asarray(list(map(lambda x: x.item(), val_acc)))\n",
+    "    \n",
+    "    np.save(path_res+'train_acc.npy', train_acc)\n",
+    "    np.save(path_res+'val_acc.npy', val_acc)\n",
+    "    np.save(path_res+'train_loss.npy', train_loss)\n",
+    "    np.save(path_res+'val_loss.npy', val_loss)\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    plt.plot(range(1,epoch_limit), train_loss, color='blue')\n",
+    "    plt.plot(range(1,epoch_limit), val_loss, color='red')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Loss')\n",
+    "    plt.title('Loss Curve')\n",
+    "    plt.legend(['Train Loss', 'Validation Loss'])\n",
+    "    plt.show()\n",
+    "    plt.clf()\n",
+    "    plt.cla()\n",
+    "    plt.close()\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    plt.plot(range(1,epoch_limit), train_acc, color='blue')\n",
+    "    plt.plot(range(1,epoch_limit), val_acc, color='red')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Accuracy')\n",
+    "    plt.title('Accuracy Curve')\n",
+    "    plt.legend(['Train Accuracy', 'Validation Accuracy'])\n",
+    "    plt.show()\n",
+    "    \n",
+    "    plt.clf()\n",
+    "    plt.cla()\n",
+    "    plt.close()\n",
+    "    torch.cuda.empty_cache()\n",
+    "    cv2.destroyAllWindows()\n",
+    "    del model\n",
+    "    gc.collect()\n",
+    "\n",
+    "    return path_res, model_name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93c136ee",
+   "metadata": {},
+   "source": [
+    "### Ensemble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "52e8d4c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch : 1\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                          | 0/337 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([], device='cuda:0', size=(4, 0))\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [4, 0]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[10], line 42\u001b[0m\n\u001b[0;32m     38\u001b[0m     model \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m     40\u001b[0m \u001b[38;5;66;03m#----------Создания датасета и обучение модели--------------\u001b[39;00m\n\u001b[1;32m---> 42\u001b[0m path_res, model_name \u001b[38;5;241m=\u001b[39m \u001b[43mprepare_and_learning_detection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_classes\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnum_classes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m20000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath_dataset\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m//192.168.11.63/data/DATASETS/Energomash/2400_learning/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m     43\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m_2.4_jpg_\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     46\u001b[0m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mempty_cache()\n\u001b[0;32m     47\u001b[0m cv2\u001b[38;5;241m.\u001b[39mdestroyAllWindows()\n",
+      "Cell \u001b[1;32mIn[2], line 108\u001b[0m, in \u001b[0;36mprepare_and_learning_detection\u001b[1;34m(num_classes, num_samples, path_dataset, model_name, config_name, model)\u001b[0m\n\u001b[0;32m    105\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m    107\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(phase \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m--> 108\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    109\u001b[0m     _, pred \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mmax(output\u001b[38;5;241m.\u001b[39mdata, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m    110\u001b[0m     loss \u001b[38;5;241m=\u001b[39m criterion(output, label)\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "Cell \u001b[1;32mIn[10], line 28\u001b[0m, in \u001b[0;36mModel.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m     27\u001b[0m     \u001b[38;5;28mprint\u001b[39m(x)\n\u001b[1;32m---> 28\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     29\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\resnet.py:285\u001b[0m, in \u001b[0;36mResNet.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 285\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\resnet.py:268\u001b[0m, in \u001b[0;36mResNet._forward_impl\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    266\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_forward_impl\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m    267\u001b[0m     \u001b[38;5;66;03m# See note [TorchScript super()]\u001b[39;00m\n\u001b[1;32m--> 268\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    269\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbn1(x)\n\u001b[0;32m    270\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelu(x)\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\conv.py:458\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    457\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 458\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\conv.py:454\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m    450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m    451\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[0;32m    452\u001b[0m                     weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[0;32m    453\u001b[0m                     _pair(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[1;32m--> 454\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    455\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[1;31mRuntimeError\u001b[0m: Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [4, 0]"
+     ]
+    }
+   ],
+   "source": [
+    "#----------Инициализируем модель и параметры обучения--------------\n",
+    "\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "gc.collect()\n",
+    "\n",
+    "config_name = \"ensemble\"\n",
+    "    \n",
+    "def load_function(attr):\n",
+    "    module_, func = attr.rsplit('.', maxsplit=1)\n",
+    "    return getattr(import_module(module_), func)\n",
+    "    \n",
+    "config = mlconfig.load('config_' + config_name + '.yaml')\n",
+    "\n",
+    "model = models.resnet18(pretrained=True)\n",
+    "\n",
+    "num_classes = 2\n",
+    "\n",
+    "model.fc = nn.Linear(model.fc.in_features, num_classes)\n",
+    "\n",
+    "class Model(nn.Module):\n",
+    "    def __init__(self, model):\n",
+    "        super(Model, self).__init__()\n",
+    "        self.model = model\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        print(x)\n",
+    "        x = self.model(x)\n",
+    "        return x\n",
+    "\n",
+    "model = Model(model)\n",
+    "\n",
+    "optimizer = load_function(config.optimizer.name)(model.parameters(), lr=config.optimizer.lr)\n",
+    "criterion = load_function(config.loss_function.name)()\n",
+    "scheduler = load_function(config.scheduler.name)(optimizer, step_size=config.scheduler.step_size, gamma=config.scheduler.gamma)\n",
+    "\n",
+    "if device != 'cpu':\n",
+    "    model = model.to(device)\n",
+    "\n",
+    "#----------Создания датасета и обучение модели--------------\n",
+    "\n",
+    "path_res, model_name = prepare_and_learning_detection(num_classes = num_classes, num_samples = 20000, path_dataset = \"//192.168.11.63/data/DATASETS/Energomash/2400_learning/\", \n",
+    "                     model_name = config_name+\"_2.4_jpg_\", config_name = config_name, model=model)\n",
+    "\n",
+    "\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "del model\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57d18676",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c10afb29",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Отсутствует",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/Training_models_2.4-Copy3.ipynb

@@ -0,0 +1,465 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5a13ad6b-56c9-4381-b376-1765f6dd7553",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Импортирование библиотек"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7311cb4a-5bf3-4268-b431-43eea10e9ed6",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from torch import default_generator, randperm\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "import torchvision.transforms as transforms\n",
+    "from torchvision.io import read_image\n",
+    "from importlib import import_module\n",
+    "import matplotlib.pyplot as plt\n",
+    "from torchvision import models\n",
+    "import torch, torchvision\n",
+    "from pathlib import Path\n",
+    "from PIL import Image\n",
+    "import torch.nn as nn\n",
+    "from tqdm import tqdm\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "import os, shutil\n",
+    "import mlconfig\n",
+    "import random\n",
+    "import shutil\n",
+    "import timeit\n",
+    "import copy\n",
+    "import time\n",
+    "import cv2\n",
+    "import csv\n",
+    "import sys\n",
+    "import io\n",
+    "import gc\n",
+    "\n",
+    "plt.rcParams[\"savefig.bbox\"] = 'tight'\n",
+    "torch.manual_seed(1)\n",
+    "#matplotlib.use('Agg')\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "print(device)\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "384de097-82c6-41f5-bda9-b2f54bc99593",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Подготовка и обучение детектирование"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "46e4dc99-6994-4fee-a32e-f3983bd991bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prepare_and_learning_detection(num_classes, num_samples, path_dataset, model_name, config_name, model):\n",
+    "    num_samples_per_class = num_samples // num_classes\n",
+    "\n",
+    "    #----------Создаём папку для сохранения результатов обучения--------------\n",
+    "    \n",
+    "    ind = 1\n",
+    "    while True:\n",
+    "        if os.path.exists(\"models/\" + model_name + str(ind)):\n",
+    "            ind += 1\n",
+    "        else:\n",
+    "            os.mkdir(\"models/\" + model_name + str(ind))\n",
+    "            path_res = \"models/\" + model_name + str(ind) + '/'\n",
+    "            break\n",
+    "    \n",
+    "    #----------Создаём файл dataset.csv для обучения--------------\n",
+    "    \n",
+    "    pd_columns = ['file_name']\n",
+    "    df = pd.DataFrame(columns=pd_columns)\n",
+    "    \n",
+    "    subdirs = os.listdir(path_dataset)\n",
+    "    for subdir in subdirs:\n",
+    "        files = os.listdir(path_dataset + subdir + '/')\n",
+    "        num_samples_per_class = min(num_samples_per_class, len(files))\n",
+    "    for subdir in subdirs:\n",
+    "        files = os.listdir(path_dataset + subdir + '/')\n",
+    "        random.shuffle(files)\n",
+    "        files_to_process = files[:num_samples_per_class]\n",
+    "        for file in files_to_process:\n",
+    "            row = pd.DataFrame({pd_columns[0]: [str(path_dataset + subdir + '/' + file)]})\n",
+    "            df = pd.concat([df, row], ignore_index=True)\n",
+    "    \n",
+    "    df.to_csv(path_res + 'dataset.csv', index=False)\n",
+    "    \n",
+    "    #----------Импортируем параметры для обучения--------------\n",
+    "    \n",
+    "    def load_function(attr):\n",
+    "        module_, func = attr.rsplit('.', maxsplit=1)\n",
+    "        return getattr(import_module(module_), func)\n",
+    "    \n",
+    "    config = mlconfig.load('config_' + config_name + '.yaml')\n",
+    "    \n",
+    "    #----------Создаём класс датасета--------------\n",
+    "    \n",
+    "    class MyDataset(Dataset):\n",
+    "        def __init__(self, path_dataset, csv_file):\n",
+    "            data=[]\n",
+    "            with open(path_dataset + csv_file, newline='') as csvfile:\n",
+    "                reader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n",
+    "                for row in list(reader)[1:]:\n",
+    "                    row = str(row)\n",
+    "                    data.append(row[2: len(row)-2])\n",
+    "            self.sig_filenames = data\n",
+    "            self.path_dataset = path_dataset\n",
+    "    \n",
+    "        def __len__(self):\n",
+    "            return len(self.sig_filenames)\n",
+    "    \n",
+    "        def __getitem__(self, idx):\n",
+    "            image_real = np.asarray(cv2.split(cv2.imread(self.sig_filenames[idx][:-8]+'real.jpg')), dtype=np.float32)\n",
+    "            if 'drone' in list(self.sig_filenames[idx].split('/')):\n",
+    "                label = torch.tensor(0)\n",
+    "            if 'noise' in list(self.sig_filenames[idx].split('/')):\n",
+    "                label = torch.tensor(1)\n",
+    "            return image_real, label\n",
+    "    \n",
+    "    #----------Создаём датасет--------------\n",
+    "    \n",
+    "    dataset = MyDataset(path_dataset=path_res, csv_file='dataset.csv')\n",
+    "    train_set, valid_set = torch.utils.data.random_split(dataset, [0.7, 0.3], generator=torch.Generator().manual_seed(42))\n",
+    "    batch_size = config.batch_size\n",
+    "    train_dataloader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "    valid_dataloader = torch.utils.data.DataLoader(valid_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "    \n",
+    "    dataloaders = {}\n",
+    "    dataloaders['train'] = train_dataloader\n",
+    "    dataloaders['val'] = valid_dataloader\n",
+    "    dataset_sizes = {}\n",
+    "    dataset_sizes['train'] = len(train_set)\n",
+    "    dataset_sizes['val'] = len(valid_set)\n",
+    "\n",
+    "    #----------Обучаем модель--------------\n",
+    "\n",
+    "    val_loss = []\n",
+    "    val_acc = []\n",
+    "    train_loss = []\n",
+    "    train_acc = []\n",
+    "    epochs = config.epoch\n",
+    "    \n",
+    "    best_acc = 0.0\n",
+    "    best_model = copy.deepcopy(model.state_dict())\n",
+    "    limit = config.limit\n",
+    "    epoch_limit = epochs\n",
+    "    \n",
+    "    start = timeit.default_timer()\n",
+    "    for epoch in range(1, epochs+1):\n",
+    "        print(f\"Epoch : {epoch}\\n\")\n",
+    "        dataloader = None\n",
+    "    \n",
+    "        for phase in ['train', 'val']:\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "    \n",
+    "            for (img, label) in tqdm(dataloaders[phase]):\n",
+    "                img, label = img.to(device), label.to(device)\n",
+    "                optimizer.zero_grad()\n",
+    "    \n",
+    "                with torch.set_grad_enabled(phase == 'train'):\n",
+    "                    output = model(img)\n",
+    "                    _, pred = torch.max(output.data, 1)\n",
+    "                    loss = criterion(output, label)\n",
+    "                    if phase=='train' :\n",
+    "                        loss.backward()\n",
+    "                        optimizer.step()\n",
+    "                \n",
+    "                running_loss += loss.item() * img.size(0)\n",
+    "                running_corrects += torch.sum(pred == label.data)\n",
+    "    \n",
+    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
+    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
+    "                \n",
+    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n",
+    "                    \n",
+    "            if phase=='train' :\n",
+    "                train_loss.append(epoch_loss)\n",
+    "                train_acc.append(epoch_acc)\n",
+    "            else :\n",
+    "                val_loss.append(epoch_loss)\n",
+    "                val_acc.append(epoch_acc)\n",
+    "                if val_acc[-1] > best_acc :\n",
+    "                    ind_limit = 0\n",
+    "                    best_acc = val_acc[-1]\n",
+    "                    best_model = copy.deepcopy(model.state_dict())\n",
+    "                    torch.save(best_model, path_res + model_name + '.pth')\n",
+    "                else:\n",
+    "                    ind_limit += 1\n",
+    "                    \n",
+    "                if ind_limit >= limit:\n",
+    "                    break\n",
+    "                \n",
+    "        if ind_limit >= limit:\n",
+    "            epoch_limit = epoch\n",
+    "            break\n",
+    "            \n",
+    "        print()\n",
+    "    \n",
+    "    end = timeit.default_timer()\n",
+    "    print(f\"Total time elapsed = {end - start} seconds\")\n",
+    "    epoch_limit += 1\n",
+    "    \n",
+    "    #----------Вывод графиков и сохранение результатов обучения--------------\n",
+    "    \n",
+    "    train_acc = np.asarray(list(map(lambda x: x.item(), train_acc)))\n",
+    "    val_acc = np.asarray(list(map(lambda x: x.item(), val_acc)))\n",
+    "    \n",
+    "    np.save(path_res+'train_acc.npy', train_acc)\n",
+    "    np.save(path_res+'val_acc.npy', val_acc)\n",
+    "    np.save(path_res+'train_loss.npy', train_loss)\n",
+    "    np.save(path_res+'val_loss.npy', val_loss)\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    plt.plot(range(1,epoch_limit), train_loss, color='blue')\n",
+    "    plt.plot(range(1,epoch_limit), val_loss, color='red')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Loss')\n",
+    "    plt.title('Loss Curve')\n",
+    "    plt.legend(['Train Loss', 'Validation Loss'])\n",
+    "    plt.show()\n",
+    "    plt.clf()\n",
+    "    plt.cla()\n",
+    "    plt.close()\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    plt.plot(range(1,epoch_limit), train_acc, color='blue')\n",
+    "    plt.plot(range(1,epoch_limit), val_acc, color='red')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Accuracy')\n",
+    "    plt.title('Accuracy Curve')\n",
+    "    plt.legend(['Train Accuracy', 'Validation Accuracy'])\n",
+    "    plt.show()\n",
+    "    \n",
+    "    plt.clf()\n",
+    "    plt.cla()\n",
+    "    plt.close()\n",
+    "    torch.cuda.empty_cache()\n",
+    "    cv2.destroyAllWindows()\n",
+    "    del model\n",
+    "    gc.collect()\n",
+    "\n",
+    "    return path_res, model_name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93c136ee",
+   "metadata": {},
+   "source": [
+    "### Ensemble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "52e8d4c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch : 1\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                          | 0/337 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([], device='cuda:0', size=(4, 0))\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [4, 0]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[10], line 42\u001b[0m\n\u001b[0;32m     38\u001b[0m     model \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m     40\u001b[0m \u001b[38;5;66;03m#----------Создания датасета и обучение модели--------------\u001b[39;00m\n\u001b[1;32m---> 42\u001b[0m path_res, model_name \u001b[38;5;241m=\u001b[39m \u001b[43mprepare_and_learning_detection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_classes\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnum_classes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m20000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath_dataset\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m//192.168.11.63/data/DATASETS/Energomash/2400_learning/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m     43\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m_2.4_jpg_\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     46\u001b[0m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mempty_cache()\n\u001b[0;32m     47\u001b[0m cv2\u001b[38;5;241m.\u001b[39mdestroyAllWindows()\n",
+      "Cell \u001b[1;32mIn[2], line 108\u001b[0m, in \u001b[0;36mprepare_and_learning_detection\u001b[1;34m(num_classes, num_samples, path_dataset, model_name, config_name, model)\u001b[0m\n\u001b[0;32m    105\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m    107\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(phase \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m--> 108\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    109\u001b[0m     _, pred \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mmax(output\u001b[38;5;241m.\u001b[39mdata, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m    110\u001b[0m     loss \u001b[38;5;241m=\u001b[39m criterion(output, label)\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "Cell \u001b[1;32mIn[10], line 28\u001b[0m, in \u001b[0;36mModel.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m     27\u001b[0m     \u001b[38;5;28mprint\u001b[39m(x)\n\u001b[1;32m---> 28\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     29\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\resnet.py:285\u001b[0m, in \u001b[0;36mResNet.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 285\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\resnet.py:268\u001b[0m, in \u001b[0;36mResNet._forward_impl\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    266\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_forward_impl\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m    267\u001b[0m     \u001b[38;5;66;03m# See note [TorchScript super()]\u001b[39;00m\n\u001b[1;32m--> 268\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    269\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbn1(x)\n\u001b[0;32m    270\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelu(x)\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\conv.py:458\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    457\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 458\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\conv.py:454\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m    450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m    451\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[0;32m    452\u001b[0m                     weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[0;32m    453\u001b[0m                     _pair(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[1;32m--> 454\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    455\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[1;31mRuntimeError\u001b[0m: Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [4, 0]"
+     ]
+    }
+   ],
+   "source": [
+    "#----------Инициализируем модель и параметры обучения--------------\n",
+    "\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "gc.collect()\n",
+    "\n",
+    "config_name = \"ensemble\"\n",
+    "    \n",
+    "def load_function(attr):\n",
+    "    module_, func = attr.rsplit('.', maxsplit=1)\n",
+    "    return getattr(import_module(module_), func)\n",
+    "    \n",
+    "config = mlconfig.load('config_' + config_name + '.yaml')\n",
+    "\n",
+    "model = models.resnet18(pretrained=True)\n",
+    "\n",
+    "num_classes = 2\n",
+    "\n",
+    "model.fc = nn.Linear(model.fc.in_features, num_classes)\n",
+    "\n",
+    "class Model(nn.Module):\n",
+    "    def __init__(self, model):\n",
+    "        super(Model, self).__init__()\n",
+    "        self.model = model\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        print(x)\n",
+    "        x = self.model(x)\n",
+    "        return x\n",
+    "\n",
+    "model = Model(model)\n",
+    "\n",
+    "optimizer = load_function(config.optimizer.name)(model.parameters(), lr=config.optimizer.lr)\n",
+    "criterion = load_function(config.loss_function.name)()\n",
+    "scheduler = load_function(config.scheduler.name)(optimizer, step_size=config.scheduler.step_size, gamma=config.scheduler.gamma)\n",
+    "\n",
+    "if device != 'cpu':\n",
+    "    model = model.to(device)\n",
+    "\n",
+    "#----------Создания датасета и обучение модели--------------\n",
+    "\n",
+    "path_res, model_name = prepare_and_learning_detection(num_classes = num_classes, num_samples = 20000, path_dataset = \"//192.168.11.63/data/DATASETS/Energomash/2400_learning/\", \n",
+    "                     model_name = config_name+\"_2.4_jpg_\", config_name = config_name, model=model)\n",
+    "\n",
+    "\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "del model\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57d18676",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c10afb29",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Отсутствует",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/Training_models_915.ipynb

@@ -0,0 +1,503 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5a13ad6b-56c9-4381-b376-1765f6dd7553",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Импортирование библиотек"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7311cb4a-5bf3-4268-b431-43eea10e9ed6",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from torch import default_generator, randperm\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "import torchvision.transforms as transforms\n",
+    "from torchvision.io import read_image\n",
+    "from importlib import import_module\n",
+    "import matplotlib.pyplot as plt\n",
+    "from torchvision import models\n",
+    "import torch, torchvision\n",
+    "from pathlib import Path\n",
+    "from PIL import Image\n",
+    "import torch.nn as nn\n",
+    "from tqdm import tqdm\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "import os, shutil\n",
+    "import mlconfig\n",
+    "import random\n",
+    "import shutil\n",
+    "import timeit\n",
+    "import copy\n",
+    "import time\n",
+    "import cv2\n",
+    "import csv\n",
+    "import sys\n",
+    "import io\n",
+    "import gc\n",
+    "\n",
+    "plt.rcParams[\"savefig.bbox\"] = 'tight'\n",
+    "torch.manual_seed(1)\n",
+    "#matplotlib.use('Agg')\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "print(device)\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "384de097-82c6-41f5-bda9-b2f54bc99593",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Подготовка и обучение детектирование"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "46e4dc99-6994-4fee-a32e-f3983bd991bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prepare_and_learning_detection(num_classes, num_samples, path_dataset, model_name, config_name, model):\n",
+    "    num_samples_per_class = num_samples // num_classes\n",
+    "\n",
+    "    #----------Создаём папку для сохранения результатов обучения--------------\n",
+    "    \n",
+    "    ind = 1\n",
+    "    while True:\n",
+    "        if os.path.exists(\"models/\" + model_name + str(ind)):\n",
+    "            ind += 1\n",
+    "        else:\n",
+    "            os.mkdir(\"models/\" + model_name + str(ind))\n",
+    "            path_res = \"models/\" + model_name + str(ind) + '/'\n",
+    "            break\n",
+    "    \n",
+    "    #----------Создаём файл dataset.csv для обучения--------------\n",
+    "    \n",
+    "    pd_columns = ['file_name']\n",
+    "    df = pd.DataFrame(columns=pd_columns)\n",
+    "    \n",
+    "    subdirs = os.listdir(path_dataset)\n",
+    "    for subdir in subdirs:\n",
+    "        files = os.listdir(path_dataset + subdir + '/')\n",
+    "        num_samples_per_class = min(num_samples_per_class, len(files))\n",
+    "    for subdir in subdirs:\n",
+    "        files = os.listdir(path_dataset + subdir + '/')\n",
+    "        random.shuffle(files)\n",
+    "        files_to_process = files[:num_samples_per_class]\n",
+    "        for file in files_to_process:\n",
+    "            row = pd.DataFrame({pd_columns[0]: [str(path_dataset + subdir + '/' + file)]})\n",
+    "            df = pd.concat([df, row], ignore_index=True)\n",
+    "    \n",
+    "    df.to_csv(path_res + 'dataset.csv', index=False)\n",
+    "    \n",
+    "    #----------Импортируем параметры для обучения--------------\n",
+    "    \n",
+    "    def load_function(attr):\n",
+    "        module_, func = attr.rsplit('.', maxsplit=1)\n",
+    "        return getattr(import_module(module_), func)\n",
+    "    \n",
+    "    config = mlconfig.load('config_' + config_name + '.yaml')\n",
+    "    \n",
+    "    #----------Создаём класс датасета--------------\n",
+    "    \n",
+    "    class MyDataset(Dataset):\n",
+    "        def __init__(self, path_dataset, csv_file):\n",
+    "            data=[]\n",
+    "            with open(path_dataset + csv_file, newline='') as csvfile:\n",
+    "                reader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n",
+    "                for row in list(reader)[1:]:\n",
+    "                    row = str(row)\n",
+    "                    data.append(row[2: len(row)-2])\n",
+    "            self.sig_filenames = data\n",
+    "            self.path_dataset = path_dataset\n",
+    "    \n",
+    "        def __len__(self):\n",
+    "            return len(self.sig_filenames)\n",
+    "    \n",
+    "        def __getitem__(self, idx):\n",
+    "            image_real = np.asarray(cv2.split(cv2.imread(self.sig_filenames[idx][:-8]+'real.jpg')), dtype=np.float32)\n",
+    "            image_imag = np.asarray(cv2.split(cv2.imread(self.sig_filenames[idx][:-8]+'imag.jpg')), dtype=np.float32)\n",
+    "            image_spec = np.asarray(cv2.split(cv2.imread(self.sig_filenames[idx][:-8]+'spec.jpg')), dtype=np.float32)\n",
+    "            if 'drone' in list(self.sig_filenames[idx].split('/')):\n",
+    "                label = torch.tensor(0)\n",
+    "            if 'noise' in list(self.sig_filenames[idx].split('/')):\n",
+    "                label = torch.tensor(1)\n",
+    "            return image_real, image_imag, image_spec, label\n",
+    "    \n",
+    "    #----------Создаём датасет--------------\n",
+    "    \n",
+    "    dataset = MyDataset(path_dataset=path_res, csv_file='dataset.csv')\n",
+    "    train_set, valid_set = torch.utils.data.random_split(dataset, [0.7, 0.3], generator=torch.Generator().manual_seed(42))\n",
+    "    batch_size = config.batch_size\n",
+    "    train_dataloader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "    valid_dataloader = torch.utils.data.DataLoader(valid_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "    \n",
+    "    dataloaders = {}\n",
+    "    dataloaders['train'] = train_dataloader\n",
+    "    dataloaders['val'] = valid_dataloader\n",
+    "    dataset_sizes = {}\n",
+    "    dataset_sizes['train'] = len(train_set)\n",
+    "    dataset_sizes['val'] = len(valid_set)\n",
+    "\n",
+    "    #----------Обучаем модель--------------\n",
+    "\n",
+    "    val_loss = []\n",
+    "    val_acc = []\n",
+    "    train_loss = []\n",
+    "    train_acc = []\n",
+    "    epochs = config.epoch\n",
+    "    \n",
+    "    best_acc = 0.0\n",
+    "    best_model = copy.deepcopy(model.state_dict())\n",
+    "    limit = config.limit\n",
+    "    epoch_limit = epochs\n",
+    "    \n",
+    "    start = timeit.default_timer()\n",
+    "    for epoch in range(1, epochs+1):\n",
+    "        print(f\"Epoch : {epoch}\\n\")\n",
+    "        dataloader = None\n",
+    "    \n",
+    "        for phase in ['train', 'val']:\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "    \n",
+    "            for (img1, img2, img3, label) in tqdm(dataloaders[phase]):\n",
+    "                img1, img2, img3, label = img1.to(device), img2.to(device), img3.to(device), label.to(device)\n",
+    "                optimizer.zero_grad()\n",
+    "    \n",
+    "                with torch.set_grad_enabled(phase == 'train'):\n",
+    "                    output = model([img1, img2, img3])\n",
+    "                    _, pred = torch.max(output.data, 1)\n",
+    "                    loss = criterion(output, label)\n",
+    "                    if phase=='train' :\n",
+    "                        loss.backward()\n",
+    "                        optimizer.step()\n",
+    "                \n",
+    "                running_loss += loss.item() * 3 * img1.size(0)\n",
+    "                running_corrects += torch.sum(pred == label.data)\n",
+    "    \n",
+    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
+    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
+    "                \n",
+    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n",
+    "                    \n",
+    "            if phase=='train' :\n",
+    "                train_loss.append(epoch_loss)\n",
+    "                train_acc.append(epoch_acc)\n",
+    "            else :\n",
+    "                val_loss.append(epoch_loss)\n",
+    "                val_acc.append(epoch_acc)\n",
+    "                if val_acc[-1] > best_acc :\n",
+    "                    ind_limit = 0\n",
+    "                    best_acc = val_acc[-1]\n",
+    "                    best_model = copy.deepcopy(model.state_dict())\n",
+    "                    torch.save(best_model, path_res + model_name + '.pth')\n",
+    "                else:\n",
+    "                    ind_limit += 1\n",
+    "                    \n",
+    "                if ind_limit >= limit:\n",
+    "                    break\n",
+    "                \n",
+    "        if ind_limit >= limit:\n",
+    "            epoch_limit = epoch\n",
+    "            break\n",
+    "            \n",
+    "        print()\n",
+    "    \n",
+    "    end = timeit.default_timer()\n",
+    "    print(f\"Total time elapsed = {end - start} seconds\")\n",
+    "    epoch_limit += 1\n",
+    "    \n",
+    "    #----------Вывод графиков и сохранение результатов обучения--------------\n",
+    "    \n",
+    "    train_acc = np.asarray(list(map(lambda x: x.item(), train_acc)))\n",
+    "    val_acc = np.asarray(list(map(lambda x: x.item(), val_acc)))\n",
+    "    \n",
+    "    np.save(path_res+'train_acc.npy', train_acc)\n",
+    "    np.save(path_res+'val_acc.npy', val_acc)\n",
+    "    np.save(path_res+'train_loss.npy', train_loss)\n",
+    "    np.save(path_res+'val_loss.npy', val_loss)\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    plt.plot(range(1,epoch_limit), train_loss, color='blue')\n",
+    "    plt.plot(range(1,epoch_limit), val_loss, color='red')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Loss')\n",
+    "    plt.title('Loss Curve')\n",
+    "    plt.legend(['Train Loss', 'Validation Loss'])\n",
+    "    plt.show()\n",
+    "    plt.clf()\n",
+    "    plt.cla()\n",
+    "    plt.close()\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    plt.plot(range(1,epoch_limit), train_acc, color='blue')\n",
+    "    plt.plot(range(1,epoch_limit), val_acc, color='red')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Accuracy')\n",
+    "    plt.title('Accuracy Curve')\n",
+    "    plt.legend(['Train Accuracy', 'Validation Accuracy'])\n",
+    "    plt.show()\n",
+    "    \n",
+    "    plt.clf()\n",
+    "    plt.cla()\n",
+    "    plt.close()\n",
+    "    torch.cuda.empty_cache()\n",
+    "    cv2.destroyAllWindows()\n",
+    "    del model\n",
+    "    gc.collect()\n",
+    "\n",
+    "    return path_res, model_name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93c136ee",
+   "metadata": {},
+   "source": [
+    "### Ensemble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52e8d4c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch : 1\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████| 658/658 [1:00:26<00:00,  5.51s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train Loss: 0.6663 Acc: 0.9241\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████| 282/282 [02:45<00:00,  1.71it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "val Loss: 0.4023 Acc: 0.9557\n",
+      "\n",
+      "Epoch : 2\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████| 658/658 [43:11<00:00,  3.94s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train Loss: 0.4096 Acc: 0.9514\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████| 282/282 [00:47<00:00,  5.98it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "val Loss: 0.3390 Acc: 0.9574\n",
+      "\n",
+      "Epoch : 3\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  8%|██████▍                                                                          | 52/658 [04:26<51:43,  5.12s/it]"
+     ]
+    }
+   ],
+   "source": [
+    "#----------Инициализируем модель и параметры обучения--------------\n",
+    "\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "gc.collect()\n",
+    "\n",
+    "num_classes = 3\n",
+    "config_name = \"ensemble\"\n",
+    "    \n",
+    "def load_function(attr):\n",
+    "    module_, func = attr.rsplit('.', maxsplit=1)\n",
+    "    return getattr(import_module(module_), func)\n",
+    "    \n",
+    "config = mlconfig.load('config_' + config_name + '.yaml')\n",
+    "\n",
+    "model1 = models.resnet18(pretrained=False)\n",
+    "model2 = models.resnet50(pretrained=False)\n",
+    "model3 = models.resnet101(pretrained=False)\n",
+    "\n",
+    "num_classes = 2\n",
+    "\n",
+    "model1.fc = nn.Linear(model1.fc.in_features, num_classes)\n",
+    "model2.fc = nn.Linear(model2.fc.in_features, num_classes)\n",
+    "model3.fc = nn.Linear(model3.fc.in_features, num_classes)\n",
+    "\n",
+    "class Ensemble(nn.Module):\n",
+    "    def __init__(self, model1, model2, model3):\n",
+    "        super(Ensemble, self).__init__()\n",
+    "        self.model1 = model1\n",
+    "        self.model2 = model2\n",
+    "        self.model3 = model3\n",
+    "        self.fc = nn.Linear(3 * num_classes, num_classes)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x1 = self.model1(x[0])\n",
+    "        x2 = self.model2(x[1])\n",
+    "        x3 = self.model3(x[2])\n",
+    "        x = torch.cat((x1, x2, x3), dim=1)\n",
+    "        x = self.fc(x)\n",
+    "        return x\n",
+    "\n",
+    "model = Ensemble(model1, model2, model3)\n",
+    "\n",
+    "optimizer = load_function(config.optimizer.name)(model.parameters(), lr=config.optimizer.lr)\n",
+    "criterion = load_function(config.loss_function.name)()\n",
+    "scheduler = load_function(config.scheduler.name)(optimizer, step_size=config.scheduler.step_size, gamma=config.scheduler.gamma)\n",
+    "\n",
+    "if device != 'cpu':\n",
+    "    model = model.to(device)\n",
+    "\n",
+    "#----------Создания датасета и обучение модели--------------\n",
+    "\n",
+    "path_res, model_name = prepare_and_learning_detection(num_classes = num_classes, num_samples = 5000, path_dataset = \"C:/Users/snytk/Lerning_NN_for_work/datasets_jpg/915_jpg_learning/\", \n",
+    "                     model_name = config_name+\"_915_jpg_\", config_name = config_name, model=model)\n",
+    "\n",
+    "\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "del model\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57d18676",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eab69324",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Отсутствует",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/Untitled.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c1b1a484",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "['290743', '1', '2025-02-14 00:24:41', '2025-02-14 00:24:39', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290740', '1', '2025-02-14 00:24:10', '2025-02-14 00:24:08', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290739', '1', '2025-02-14 00:24:00', '2025-02-14 00:23:58', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290736', '1', '2025-02-14 00:23:29', '2025-02-14 00:23:27', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290735', '1', '2025-02-14 00:23:24', '2025-02-14 00:23:22', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290732', '1', '2025-02-14 00:22:53', '2025-02-14 00:22:51', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290728', '1', '2025-02-14 00:22:20', '2025-02-14 00:22:18', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290727', '1', '2025-02-14 00:22:17', '2025-02-14 00:22:15', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290724', '1', '2025-02-14 00:21:46', '2025-02-14 00:21:44', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290721', '1', '2025-02-14 00:21:15', '2025-02-14 00:21:13', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290720', '1', '2025-02-14 00:21:10', '2025-02-14 00:21:08', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290717', '1', '2025-02-14 00:20:39', '2025-02-14 00:20:37', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
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+      "['290713', '1', '2025-02-14 00:20:02', '2025-02-14 00:20:00', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290712', '1', '2025-02-14 00:20:00', '2025-02-14 00:19:58', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290709', '1', '2025-02-14 00:19:29', '2025-02-14 00:19:27', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290705', '1', '2025-02-14 00:18:56', '2025-02-14 00:18:54', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290704', '1', '2025-02-14 00:18:48', '2025-02-14 00:18:46', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290701', '1', '2025-02-14 00:18:17', '2025-02-14 00:18:15', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290700', '1', '2025-02-14 00:18:12', '2025-02-14 00:18:10', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290697', '1', '2025-02-14 00:17:41', '2025-02-14 00:17:39', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290693', '1', '2025-02-14 00:17:08', '2025-02-14 00:17:06', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290692', '1', '2025-02-14 00:17:06', '2025-02-14 00:17:03', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290689', '1', '2025-02-14 00:16:34', '2025-02-14 00:16:32', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290685', '1', '2025-02-14 00:16:01', '2025-02-14 00:15:59', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290682', '1', '2025-02-14 00:15:30', '2025-02-14 00:15:28', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290676', '1', '2025-02-14 00:09:52', '2025-02-14 00:09:50', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290673', '1', '2025-02-14 00:09:21', '2025-02-14 00:09:19', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290672', '1', '2025-02-14 00:09:19', '2025-02-14 00:09:17', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290669', '1', '2025-02-14 00:08:48', '2025-02-14 00:08:46', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290661', '1', '2025-02-14 00:00:53', '2025-02-14 00:00:51', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290658', '1', '2025-02-14 00:00:22', '2025-02-14 00:00:19', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290654', '1', '2025-02-13 23:59:45', '2025-02-13 23:59:43', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290650', '1', '2025-02-13 23:59:12', '2025-02-13 23:59:10', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290649', '1', '2025-02-13 23:59:08', '2025-02-13 23:59:06', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290646', '1', '2025-02-13 23:58:37', '2025-02-13 23:58:35', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290645', '1', '2025-02-13 23:58:28', '2025-02-13 23:58:26', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290642', '1', '2025-02-13 23:57:57', '2025-02-13 23:57:55', '[{\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
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+      "['290043', '1', '2025-02-13 16:37:58', '2025-02-13 16:37:56', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
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+      "['290024', '1', '2025-02-13 16:35:05', '2025-02-13 16:35:03', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290023', '1', '2025-02-13 16:35:00', '2025-02-13 16:34:58', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
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+      "['290019', '1', '2025-02-13 16:34:26', '2025-02-13 16:34:24', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['290016', '1', '2025-02-13 16:33:55', '2025-02-13 16:33:53', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289835', '1', '2025-02-13 13:32:33', '2025-02-13 13:32:31', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289832', '1', '2025-02-13 13:32:02', '2025-02-13 13:31:59', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289831', '1', '2025-02-13 13:31:25', '2025-02-13 13:31:23', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289828', '1', '2025-02-13 13:30:54', '2025-02-13 13:30:52', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289827', '1', '2025-02-13 13:30:52', '2025-02-13 13:30:50', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289824', '1', '2025-02-13 13:30:21', '2025-02-13 13:30:19', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289823', '1', '2025-02-13 13:30:16', '2025-02-13 13:30:14', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
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+      "['289819', '1', '2025-02-13 13:29:41', '2025-02-13 13:29:39', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289816', '1', '2025-02-13 13:29:10', '2025-02-13 13:29:08', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289815', '1', '2025-02-13 13:29:03', '2025-02-13 13:29:01', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289812', '1', '2025-02-13 13:28:32', '2025-02-13 13:28:30', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289811', '1', '2025-02-13 13:28:30', '2025-02-13 13:28:27', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289808', '1', '2025-02-13 13:27:58', '2025-02-13 13:27:56', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289807', '1', '2025-02-13 13:27:38', '2025-02-13 13:27:36', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289804', '1', '2025-02-13 13:27:07', '2025-02-13 13:27:05', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289749', '1', '2025-02-13 12:34:53', '2025-02-13 12:34:51', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289745', '1', '2025-02-13 12:34:20', '2025-02-13 12:34:18', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289741', '1', '2025-02-13 12:33:44', '2025-02-13 12:33:42', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289737', '1', '2025-02-13 12:33:11', '2025-02-13 12:33:09', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289736', '1', '2025-02-13 12:33:06', '2025-02-13 12:33:04', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289733', '1', '2025-02-13 12:32:35', '2025-02-13 12:32:33', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289729', '1', '2025-02-13 12:32:02', '2025-02-13 12:32:00', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289728', '1', '2025-02-13 12:31:57', '2025-02-13 12:31:54', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289725', '1', '2025-02-13 12:31:25', '2025-02-13 12:31:23', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289721', '1', '2025-02-13 12:30:52', '2025-02-13 12:30:50', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289720', '1', '2025-02-13 12:30:47', '2025-02-13 12:30:45', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
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+      "['289713', '1', '2025-02-13 12:29:43', '2025-02-13 12:29:41', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289712', '1', '2025-02-13 12:29:38', '2025-02-13 12:29:36', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
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+      "['289705', '1', '2025-02-13 12:28:34', '2025-02-13 12:28:31', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289704', '1', '2025-02-13 12:28:28', '2025-02-13 12:28:26', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289701', '1', '2025-02-13 12:27:57', '2025-02-13 12:27:55', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289697', '1', '2025-02-13 12:27:24', '2025-02-13 12:27:22', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289693', '1', '2025-02-13 12:26:50', '2025-02-13 12:26:48', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289692', '1', '2025-02-13 12:26:46', '2025-02-13 12:26:44', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289689', '1', '2025-02-13 12:26:15', '2025-02-13 12:26:13', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289685', '1', '2025-02-13 12:25:41', '2025-02-13 12:25:39', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289684', '1', '2025-02-13 12:25:37', '2025-02-13 12:25:34', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 8, \"light_len\": 8, \"triggered\": true}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289681', '1', '2025-02-13 12:25:05', '2025-02-13 12:25:03', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289680', '1', '2025-02-13 12:25:00', '2025-02-13 12:24:58', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289677', '1', '2025-02-13 12:24:29', '2025-02-13 12:24:27', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289673', '1', '2025-02-13 12:23:56', '2025-02-13 12:23:54', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289669', '1', '2025-02-13 12:23:22', '2025-02-13 12:23:20', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289665', '1', '2025-02-13 12:22:48', '2025-02-13 12:22:46', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289664', '1', '2025-02-13 12:22:42', '2025-02-13 12:22:40', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289661', '1', '2025-02-13 12:22:11', '2025-02-13 12:22:08', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289657', '1', '2025-02-13 12:21:37', '2025-02-13 12:21:35', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289656', '1', '2025-02-13 12:21:34', '2025-02-13 12:21:32', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289653', '1', '2025-02-13 12:21:03', '2025-02-13 12:21:01', '[{\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289652', '1', '2025-02-13 12:21:00', '2025-02-13 12:20:58', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289649', '1', '2025-02-13 12:20:29', '2025-02-13 12:20:27', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289648', '1', '2025-02-13 12:20:23', '2025-02-13 12:20:21', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289645', '1', '2025-02-13 12:19:52', '2025-02-13 12:19:50', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289644', '1', '2025-02-13 12:19:50', '2025-02-13 12:19:48', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289641', '1', '2025-02-13 12:19:19', '2025-02-13 12:19:17', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289640', '1', '2025-02-13 12:19:16', '2025-02-13 12:19:13', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289637', '1', '2025-02-13 12:18:44', '2025-02-13 12:18:42', '[{\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289636', '1', '2025-02-13 12:18:41', '2025-02-13 12:18:39', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289633', '1', '2025-02-13 12:18:10', '2025-02-13 12:18:08', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289632', '1', '2025-02-13 12:18:04', '2025-02-13 12:18:02', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289629', '1', '2025-02-13 12:17:33', '2025-02-13 12:17:31', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289628', '1', '2025-02-13 12:17:31', '2025-02-13 12:17:29', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289625', '1', '2025-02-13 12:17:00', '2025-02-13 12:16:58', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289624', '1', '2025-02-13 12:16:58', '2025-02-13 12:16:56', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289621', '1', '2025-02-13 12:16:27', '2025-02-13 12:16:25', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289620', '1', '2025-02-13 12:16:24', '2025-02-13 12:16:22', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289617', '1', '2025-02-13 12:15:53', '2025-02-13 12:15:50', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289616', '1', '2025-02-13 12:15:45', '2025-02-13 12:15:43', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289613', '1', '2025-02-13 12:15:14', '2025-02-13 12:15:12', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289612', '1', '2025-02-13 12:15:11', '2025-02-13 12:15:09', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289609', '1', '2025-02-13 12:14:40', '2025-02-13 12:14:38', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289606', '1', '2025-02-13 12:14:09', '2025-02-13 12:14:07', '[{\"freq\": 433, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289605', '1', '2025-02-13 12:14:05', '2025-02-13 12:14:03', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['289602', '1', '2025-02-13 12:13:34', '2025-02-13 12:13:32', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 868, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "['287824', '1', '2025-02-12 04:41:04', '2025-02-12 04:41:02', '[{\"freq\": 433, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 700, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 868, \"amplitude\": 9, \"triggered\": true}, {\"freq\": 915, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 1200, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 2400, \"amplitude\": 0, \"light_len\": 0, \"triggered\": false}, {\"freq\": 5200, \"amplitude\": 0, \"triggered\": false}, {\"freq\": 5800, \"amplitude\": 0, \"triggered\": false}]', 'NULL', 'freq']\n",
+      "Данные успешно обработаны и сохранены в History_change_1.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "import csv\n",
+    "\n",
+    "# Путь к входному CSV-файлу\n",
+    "input_file = 'History_1.csv'\n",
+    "\n",
+    "# Путь к выходному CSV-файлу (с правильно разделёнными данными)\n",
+    "output_file = 'History_change_1.csv'\n",
+    "\n",
+    "# Открываем исходный файл для чтения и новый файл для записи\n",
+    "with open(input_file, mode='r', encoding='utf-8') as infile:\n",
+    "    with open(output_file, mode='w', encoding='utf-8-sig', newline='') as outfile:\n",
+    "\n",
+    "        # Создаем объект reader для чтения CSV с учетом кавычек\n",
+    "        reader = csv.reader(infile, delimiter=',', quotechar='\"')\n",
+    "\n",
+    "        # Создаем объект writer для записи в новый CSV-файл\n",
+    "        writer = csv.writer(outfile, delimiter=';', quotechar=\"'\", quoting=csv.QUOTE_MINIMAL)\n",
+    "\n",
+    "        # Проходимся по каждой строке в исходном файле\n",
+    "        for row in reader:\n",
+    "            # Записываем строку в выходной файл\n",
+    "            print(row)\n",
+    "            writer.writerow(row)\n",
+    "\n",
+    "print(f\"Данные успешно обработаны и сохранены в {output_file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "caf7679f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97cfbd11",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/config_ensemble.yaml

@@ -1,5 +1,5 @@
 # Training params
-epoch : 5
+epoch : 10
 batch_size : 4
 num_workers: 8
 limit : 5
@@ -16,5 +16,5 @@ loss_function :
 # Scheduler
 scheduler :
   name : torch.optim.lr_scheduler.StepLR
-  gamma : 0.15
-  step_size : 3
+  gamma : 0.01
+  step_size : 10

train_scripts/models/ensemble_1.2_jpg_10/dataset.csv

@@ -0,0 +1,964 @@
+file_name
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+/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1355.npy.npy
+/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1314.npy.npy
+/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_33.npy.npy
+/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1828.npy.npy
+/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_135.npy.npy
+/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_514.npy.npy
+/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_121.npy.npy
+/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_149.npy.npy
+/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1345.npy.npy
+/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1805.npy.npy
+/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_743.npy.npy

train_scripts/my_csv_make.ipynb

@@ -0,0 +1,58 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "537ea5d0-1d6c-423e-9417-171b70a76c66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "path = '//192.168.11.63/data/DATASETS/Energomash/915_learning'\n",
+    "pd_columns = ['file_name']\n",
+    "df = pd.DataFrame(columns=pd_columns)\n",
+    "\n",
+    "p = 0\n",
+    "for i in os.walk(path):\n",
+    "    p+=1\n",
+    "    if p != 1:\n",
+    "        for j in i[2]:\n",
+    "            row = pd.DataFrame({pd_columns[0]: [str(str(i[0]) + '\\\\' + str(j)).replace('\\\\', '/')]})\n",
+    "            df = pd.concat([df, row], ignore_index=True)\n",
+    "\n",
+    "df.to_csv(path + '\\dataset.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f6e1ff8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/triangulation_accurate.ipynb

@@ -0,0 +1,91 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "689613d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "80.58750000000002\n",
+      "(array([8.97705408, 4.95      , 0.        ]), array([-8.97705408,  4.95      ,  0.        ]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy  \n",
+    "from math import sqrt as square\n",
+    "from numpy import sqrt, dot, cross                       \n",
+    "from numpy.linalg import norm  \n",
+    "\n",
+    "#rssi = [rssi, max_rssi, min_rssi, gamma]\n",
+    "\n",
+    "def dist(rssi):\n",
+    "    rssi = list(map(float, rssi))\n",
+    "    return square(abs(rssi[0]-rssi[1]))*rssi[3]/square(abs(rssi[0]-rssi[2]))\n",
+    "\n",
+    "def sol(x1,x2,x3,rssi1,rssi2,rssi3):\n",
+    "    r1 = dist(rssi1)\n",
+    "    r2 = dist(rssi2)\n",
+    "    r3 = dist(rssi3)\n",
+    "    x1=numpy.array(x1)\n",
+    "    x2=numpy.array(x2)\n",
+    "    x3=numpy.array(x3)\n",
+    "    temp1 = x2-x1                                        \n",
+    "    e_x = temp1/norm(temp1)                              \n",
+    "    temp2 = x3-x1                                        \n",
+    "    i = dot(e_x,temp2)                                   \n",
+    "    temp3 = temp2 - i*e_x                                \n",
+    "    e_y = temp3/norm(temp3)                              \n",
+    "    e_z = cross(e_x,e_y)                                 \n",
+    "    d = norm(x2-x1)                                      \n",
+    "    j = dot(e_y,temp2)                                   \n",
+    "    x = (r1*r1 - r2*r2 + d*d) / (2*d)                    \n",
+    "    y = (r1*r1 - r3*r3 -2*i*x + i*i + j*j) / (2*j)       \n",
+    "    temp4 = r1*r1 - x*x - y*y \n",
+    "    print(temp4)\n",
+    "    if temp4<0:                                          \n",
+    "        return \"Нет пересечения!\"\n",
+    "    z = sqrt(temp4)                                      \n",
+    "    p_12_a = x1 + x*e_x + y*e_y + z*e_z                  \n",
+    "    p_12_b = x1 + x*e_x + y*e_y - z*e_z                  \n",
+    "    return p_12_a,p_12_b\n",
+    "\n",
+    "\n",
+    "print(sol([0,0,1],[0,0,-1],[0,10,0],[50,100,0,10.3],[50,100,0,10.3],[50,100,0,10.3]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a68f35a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train_scripts/triangulation_direction_2D.ipynb

@@ -0,0 +1,57 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f18afe4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy  \n",
+    "from math import sqrt as square\n",
+    "from numpy import sqrt, dot, cross                       \n",
+    "from numpy.linalg import norm  \n",
+    "\n",
+    "#rssi = [rssi, max_rssi, min_rssi, gamma]\n",
+    "\n",
+    "def dist(rssi):\n",
+    "    rssi = list(map(float, rssi))\n",
+    "    return square(abs(rssi[0]-rssi[1]))*rssi[3]/square(abs(rssi[0]-rssi[2]))\n",
+    "\n",
+    "def sol(x1,x2,x3,rssi1,rssi2,rssi3):\n",
+    "    r1 = dist(rssi1)\n",
+    "    r2 = dist(rssi2)\n",
+    "    r3 = dist(rssi3)\n",
+    "    x1=numpy.array(x1)\n",
+    "    x2=numpy.array(x2)\n",
+    "    x3=numpy.array(x3)\n",
+    "    \n",
+    "    return sector.\n",
+    "\n",
+    "\n",
+    "print(sol([0,0,1],[0,0,-1],[0,10,0],[50,100,0,10.3],[50,100,0,10.3],[50,100,0,10.3]))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}