marge

.gitignore

@@ -169,5 +169,6 @@ log/
 calibrate/
 experiment.py
 nohup.out
-cache.jsonl
 quick_test.py
+*.jsonl
+**/config.yaml

pyduino/config.template.yaml

@@ -0,0 +1,84 @@
+hyperparameters:
+  reset_density: true #Whether or not to use gotod
+  log_name: #Log folder name (delete to use the default)
+  density_param: DensidadeAtual #Parameter name for density counts.
+  brilho_param: Brilho #When Brilho = 0, optimization is turned off.
+  maximize: false
+  rng_seed: 2 #Random seed parameter initialization
+  ranges:
+    branco:
+      - 1
+      - 100                                     
+    others:
+      - 0
+      - 100
+slave:
+  port: "5000" #Must be a string
+  network: "192.168.1.1/24"
+  exclude: #Leave blank if there are none to exclude
+system:
+  #About the partition system (This is done at `init_spectra.py`)
+  #`all` will create a single manager for all reactors.
+  #`single` will create a manager for each reactor.
+  partition: all
+  log_level: "DEBUG" #DEBUG, INFO, WARNING, ERROR, CRITICAL
+  sync_clocks: false #Whether or not to sync the clocks of the slaves.
+  initial_state: "preset_state.d/all.csv"
+  reboot_wait_time: 5 #Time in seconds to wait after a reboot.
+  relevant_parameters:
+    # - brilho
+    - branco
+    - full
+    - "440"
+    - "470"
+    - "495"
+    - "530"
+    - "595"
+    - "634"
+    - "660"
+    - "684"
+  irradiance:
+    #brilho: 12
+    "branco": 10.35
+    "full": 11.50
+    "440": 7.72
+    "470": 8.50
+    "495": 8.30
+    "530": 8.56
+    "595": 2.17
+    "634": 3.44
+    "660": 4.52
+    "684": 5.74
+  standard_parameters:
+    #brilho: float
+    branco: float
+    full: float
+    "440": float
+    "470": float
+    "495": float
+    "530": float
+    "595": float
+    "634": float
+    "660": float
+    "684": float
+    cor: int
+    modopainel: int
+    brilho: float
+    bomdia: int
+    boanoite: int
+    tau: int
+    step: int
+    modotemp: int
+    temp: int
+    densidade: float
+    mododil: int
+    ar: int
+    ima: int
+    modoco2: int
+    co2: int
+    dtco2: int
+tensorboard:
+  additional_parameters:
+    - Brilho
+    - Temp
+    - pH

pyduino/log.py

@@ -4,7 +4,6 @@
 from datetime import datetime
 import io
 from glob import glob
-from uuid import uuid1
 from tabulate import tabulate
 from collections import OrderedDict
 from datetime import datetime
@@ -37,7 +36,7 @@ def to_markdown_table(data: OrderedDict) -> str:
 def y_to_table(y):
     return tabulate(list(y.items()), tablefmt="pipe")
 
-class log:
+class Log:
     @property
     def timestamp(self):
         """str: Current date."""
@@ -49,15 +48,15 @@ def prefix(self):
 
     def __init__(self,subdir,path="./log",name=None):
         """
-        Logs data into csvs with timestamps.
+        Logs data into jsonls with timestamps.
 
         Example:
             log_obj = log(['reactor_0','reactor_1'],path='./log',name='experiment_0')
 
             log/YEAR/MONTH/
             ├─ experiment_0/
-            │  ├─ reactor_0.csv
-            │  ├─ reactor_1.csv
+            │  ├─ reactor_0.jsonl
+            │  ├─ reactor_1.jsonl
 
         Args:
             subdir (:obj:`list` of :obj:`str`): List of the names for the subdirectories of `path`.
@@ -75,7 +74,7 @@ def __init__(self,subdir,path="./log",name=None):
             self.subdir = subdir
         else:
             raise ValueError("Invalid type for subdir. Must be either a list of strings or a glob string.")
-        self.subdir = list(map(lambda x: str(x)+".csv" if len(os.path.splitext(str(x))[1])==0 else str(x),self.subdir))
+        self.subdir = list(map(lambda x: str(x)+".jsonl" if len(os.path.splitext(str(x))[1])==0 else str(x),self.subdir))
         self.first_timestamp = None
         self.data_frames = {}
 
@@ -88,7 +87,7 @@ def __init__(self,subdir,path="./log",name=None):
             self.subdir = subdir
         else:
             raise ValueError("Invalid type for subdir. Must be either a list of strings or a glob string.")
-        self.subdir = list(map(lambda x: str(x)+".csv" if len(os.path.splitext(str(x))[1])==0 else str(x),self.subdir))
+        self.subdir = list(map(lambda x: str(x)+".jsonl" if len(os.path.splitext(str(x))[1])==0 else str(x),self.subdir))
         self.first_timestamp = None
         self.data_frames = {}
 
@@ -100,19 +99,18 @@ def backup_config_file(self):
             with open(config_file) as cfile, open(filename,'w') as wfile:
                 wfile.write(cfile.read())
 
-    def log_rows(self,rows,subdir,add_timestamp=True,tags=None,**kwargs):
+    def log_rows(self,rows,subdir,add_timestamp=True,tags=None):
         """
-        Logs rows into csv format.
+        Logs rows into jsonl format.
 
         Args:
             rows (:obj:`list` of :obj:`dict`): List of dictionary-encoded rows or pandas dataframe.
             subdir (str): Subdirectory name. Intended to be an element of `self.subdir`.
             add_timestamp (bool,optional): Whether or not to include a timestamp column.
             tags (:obj:`dict` of :obj:`str`): Dictionary of strings to be inserted as constant columns.
-            **kwargs: Additional arguments passed to `pandas.to_csv`.
         """
         t = self.timestamp
-        path = os.path.join(self.path,self.start_timestamp,f"{subdir}.csv")
+        path = os.path.join(self.path,self.start_timestamp,f"{subdir}.jsonl")
 
         df = pd.DataFrame()
         if isinstance(rows,list):
@@ -125,7 +123,7 @@ def log_rows(self,rows,subdir,add_timestamp=True,tags=None,**kwargs):
         if os.path.exists(path):
             if self.first_timestamp is None:
                 with open(path) as file:
-                    head = pd.read_csv(io.StringIO(file.readline()+file.readline()),index_col=False,**kwargs)
+                    head = pd.read_json(io.StringIO(file.readline()+file.readline()), orient="records", lines=True)
                     self.first_timestamp = datetime_from_str(head.log_timestamp[0])
         else:
             self.first_timestamp = t
@@ -136,25 +134,21 @@ def log_rows(self,rows,subdir,add_timestamp=True,tags=None,**kwargs):
             for key,value in tags.items():
                 df.loc[:,key] = value
 
-        df.to_csv(
-            path,
-            mode="a",
-            header=not os.path.exists(path),
-            index=False,
-            **kwargs
-            )
+        with open(path, mode="a") as log_file:
+            log_file.write(df.to_json(orient="records", lines=True))
+
         return df
     def log_many_rows(self,data,**kwargs):
         """
-        Logs rows into csv format.
+        Logs rows into jsonl format.
 
         Args:
             data (:obj:`dict` of :obj:`dict`): Dictionary encoded data frame.
             **kwargs: Additional arguments passed to `self.log_rows`.
         """
         self.data_frames = {}
         for _id,row in data.items():
-            df = self.log_rows(rows=[row],subdir=_id,sep='\t',**kwargs)
+            df = self.log_rows(rows=[row],subdir=_id,**kwargs)
             self.data_frames[_id] = df
         self.data_frames = pd.concat(list(self.data_frames.values()))
 
@@ -164,7 +158,7 @@ def log_optimal(self,column,maximum=True,**kwargs):
         """
         i=self.data_frames.loc[:,column].astype(float).argmax() if maximum else self.data_frames.loc[:,column].astype(float).argmin()
         self.df_opt = self.data_frames.iloc[i,:]
-        self.log_rows(rows=[self.df_opt.to_dict()],subdir='opt',sep='\t',**kwargs)
+        self.log_rows(rows=[self.df_opt.to_dict()],subdir='opt',**kwargs)
 
     def log_average(self, cols: list, **kwargs):
         """
@@ -178,38 +172,38 @@ def log_average(self, cols: list, **kwargs):
         df.loc[:, cols] = df.loc[:, cols].astype(float)
         df.elapsed_time_hours = df.elapsed_time_hours.round(decimals=2)
         self.df_avg = df.loc[:, cols + ['elapsed_time_hours']].groupby("elapsed_time_hours").mean().reset_index()
-        self.log_rows(rows=self.df_avg, subdir='avg', sep='\t', **kwargs)
+        self.log_rows(rows=self.df_avg, subdir='avg',  **kwargs)
 
-    def cache_data(self,rows,path="./cache.csv",**kwargs):
+    def cache_data(self,rows,path="./cache.jsonl",**kwargs):
         """
-        Dumps rows into a single csv.
+        Dumps rows into a single jsonl.
 
         Args:
             rows (:obj:`list` of :obj:`dict`): List of dictionary-encoded rows.
-            path (str): Path to the csv file.
+            path (str): Path to the jsonl file.
         """
-        pd.DataFrame(rows).T.to_csv(path,**kwargs)
+        pd.DataFrame(rows).T.to_json(path, orient="records", lines=True, **kwargs)
 
-    def transpose(self,columns,destination,sep='\t',skip=1,**kwargs):
+    def transpose(self,columns,destination,skip=1,**kwargs):
         """
-        Maps reactor csv to column csvs with columns given by columns.
+        Maps reactor jsonl to column jsonls with columns given by columns.
 
         Args:
             columns (:obj:list of :obj:str): List of columns to extract.
             destination (str): Destination path. Creates directories as needed and overwrites any existing files.
-            sep (str, optional): Column separator. Defaults to '\t'.
+
             skip (int, optional): How many rows to jump while reading the input files. Defaults to 1.
         """
         dfs = []
         for file in self.paths:
-            df = pd.read_csv(file,index_col=False,sep=sep,**kwargs)
+            df = pd.read_json(file, orient="records", lines=True, **kwargs)
             df['FILE'] = file
             dfs.append(df.iloc[::skip,:])
         df = pd.concat(dfs)
 
         for column in columns:
             Path(destination).mkdir(parents=True,exist_ok=True)
-            df.loc[:,['ID','FILE',column,'elapsed_time_hours']].to_csv(os.path.join(destination,f"{column}.csv"),sep=sep)
+            df.loc[:,['ID','FILE',column,'elapsed_time_hours']].to_json(os.path.join(destination,f"{column}.jsonl"), orient="records", lines=True)
 
 
 class LogAggregator:
@@ -225,20 +219,19 @@ def __init__(self,log_paths,timestamp_col="log_timestamp",elapsed_time_col="elap
         self.glob_list = log_paths
         self.timestamp_col = timestamp_col
         self.elapsed_time_col = elapsed_time_col
-    def agg(self,destination,skip=1,sep='\t',**kwargs):
+    def agg(self,destination,skip=1,**kwargs):
         """
         Aggregator
 
         Args:
             destination (str): Destination path. Creates directories as needed and overwrites any existing files.
             skip (int, optional): How many rows to jump while reading the input files. Defaults to 1.
-            sep (str, optional): Column separator. Defaults to '\t'.
         """
         dfs = {}
         for path in self.glob_list:
             for file in glob(path):
                 basename = os.path.basename(file)
-                df = pd.read_csv(file,index_col=False,sep=sep,dtype={self.elapsed_time_col:float},**kwargs)
+                df = pd.read_json(file, orient="records", lines=True, dtype={self.elapsed_time_col:float},**kwargs)
                 df = df.iloc[::skip,:]
                 df['FILE'] = file
                 if dfs.get(basename,None) is not None:
@@ -256,5 +249,5 @@ def agg(self,destination,skip=1,sep='\t',**kwargs):
         for filename, df in dfs.items():
             Path(destination).mkdir(parents=True,exist_ok=True)
             path = os.path.join(destination,filename)
-            df.to_csv(path,sep=sep,index=False)
+            df.to_json(path, orient="records", lines=True)
 

pyduino/pyduino2.py

@@ -3,7 +3,7 @@
 from time import sleep, time
 import pandas as pd
 from collections import OrderedDict
-from pyduino.log import log
+from pyduino.log import Log
 import pandas as pd
 from multiprocessing import Pool, Process
 from functools import partial
@@ -227,11 +227,10 @@ def send(self,command,await_response=True,**kwargs):
                 r._send(command)
         return out
 
-    def send_parallel(self,command,delay,await_response=True):
-        out = []
+    def send_parallel(self,command,await_response=True):
         with Pool(7) as p:
             out = p.map(partial(send_wrapper,command=command,await_response=await_response),list(self.reactors.items()))
-        return out
+        return dict(out)
 
     def set(self, data=None, **kwargs):
         for k,r in self.reactors.items():
@@ -268,9 +267,18 @@ def log_init(self,**kwargs):
         Args:
             name (str): Name of the subdirectory in the log folder where the files will be saved.
         """
-        self.log = log(subdir=list(self.reactors.keys()),**kwargs)
+        self.log = Log(subdir=list(self.reactors.keys()),**kwargs)
         print(f"Log will be saved on: {bcolors.OKGREEN}{self.log.prefix}{bcolors.ENDC}")
 
+    @property
+    def brilho(self):
+        """
+        Convenience method to get brilho from reactors.
+        """
+        out = self.send_parallel(f"get({self.brilho_param.lower()})")
+        out = {k: float(v.strip()) for k,v in out.items()}
+        return out
+
     def dados(self,save_cache=True):
         """
         Get data from Arduinos.
@@ -284,7 +292,7 @@ def dados(self,save_cache=True):
 
         len_empty = None
         while len_empty!=0:
-            rows = self.send_parallel("dados",delay=20,await_response=True)
+            rows = self.send_parallel("dados",await_response=True).items()
             #Checking if any reactor didn't respond.
             empty = list(filter(lambda x: x[1] is None,rows))
             len_empty = len(empty)
@@ -305,7 +313,7 @@ def dados(self,save_cache=True):
 
         rows = dict(map(lambda x: (x[0],OrderedDict(zip(self.header,x[1].split(" ")))),rows))
         if save_cache:
-            self.log.cache_data(rows,sep='\t',index=False) #Index set to False because ID already exists in rows.
+            self.log.cache_data(rows) #Index set to False because ID already exists in rows.
         return rows
 
     def log_dados(self,save_cache=True):
@@ -322,10 +330,10 @@ def log_dados(self,save_cache=True):
         rows = list(map(lambda x: (x[0],OrderedDict(zip(header,x[1].split(" ")))),rows))
 
         for _id,row in rows:
-            self.log.log_rows(rows=[row],subdir=_id,sep='\t')
+            self.log.log_rows(rows=[row],subdir=_id)
         rows = dict(rows)
         if save_cache:
-            self.log.cache_data(rows,sep='\t',index=False) #Index set to False because ID already exists in rows.
+            self.log.cache_data(rows) #Index set to False because ID already exists in rows.
         return rows
 
     def set_preset_state(self,path="preset_state.csv",sep="\t",chunksize=4, params=PATHS.REACTOR_PARAMETERS, **kwargs):