Update quinine and agent

scripts/_data_analysis.py

@@ -222,9 +222,11 @@ def _1peak_fit_good_PL(x0, y0, fit_function, peak=False, maxfev=100000, fit_boun
     if dummy_test:
         x = x0[w1:w2]
         y = y0[w1:w2]
+        x_low_bnd = x0[w1]
     else:         
         x = x0[w2:w3]
         y = y0[w2:w3]
+        x_low_bnd = x0[w2]
 
     mean = sum(x * y) / sum(y)
     sigma = np.sqrt(sum(abs(y) * (x - mean) ** 2) / sum(y))
@@ -237,7 +239,7 @@ def _1peak_fit_good_PL(x0, y0, fit_function, peak=False, maxfev=100000, fit_boun
 
 
     try:
-        bnd = ((0,200,0),(y.max()*1.15,1000, np.inf))
+        bnd = ((0, x_low_bnd, 0),(y.max()*1.15, 1000, np.inf))
         popt, pcov = curve_fit(fit_function, x, y, p0=initial_guess, bounds=bnd, maxfev=maxfev)
     except (RuntimeError, ValueError):
         bnd = (-np.inf, np.inf)

scripts/_data_export.py

@@ -95,6 +95,38 @@ def _auto_name_sample2(pump_list, prefix=None):
     return sample
 
 
+
+def _exprot_rate_agent(metadata_dic, rate_label_dic, agent_data):
+
+    is_infusing = []
+    for pump_status in metadata_dic['pump_status']:
+        if pump_status == 'Infusing':
+            is_infusing.append(1)
+        elif pump_status == 'Idle':
+            is_infusing.append(0)
+
+    for key in rate_label_dic.keys():
+        is_key_in_precursors = np.asarray([key in precursor for precursor in metadata_dic['precursors']])
+        num_idx = np.argwhere(is_key_in_precursors==True)
+
+        if num_idx.size == 1:
+            pre_idx = num_idx[0][0]
+            try:
+                ruc = rate_unit_converter(r0 = metadata_dic["infuse_rate_unit"][pre_idx], r1 = 'ul/min')
+                agent_data[rate_label_dic[key]] = metadata_dic["infuse_rate"][pre_idx]*ruc*is_infusing[pre_idx]
+            except IndexError:
+                agent_data[rate_label_dic[key]] = 0
+
+        elif num_idx.size == 0:
+            agent_data[rate_label_dic[key]] = 0
+
+        elif num_idx.size > 1:
+            raise ValueError (f'Check key: {key} of rate_label_dic. More than one precursor was found.')    
+
+    return agent_data
+
+
+
 def _readable_time(unix_time):
     from datetime import datetime
     dt = datetime.fromtimestamp(unix_time)
@@ -132,6 +164,7 @@ def read_qepro_by_stream(uid, stream_name='primary', data_agent='tiled'):
         except NameError:
             import databroker
             catalog = databroker.catalog['xpd-ldrd20-31']
+            # catalog = databroker.catalog['xpd']
         run = catalog[uid]
         meta = run.metadata
 
@@ -141,6 +174,8 @@ def read_qepro_by_stream(uid, stream_name='primary', data_agent='tiled'):
         except NameError:
             from tiled.client import from_profile
             tiled_client = from_profile("xpd-ldrd20-31")
+            # tiled_client = from_profile("xpd")
+
         run = tiled_client[uid]
         meta = run.metadata
 
@@ -456,7 +491,8 @@ def read_qepro_from_tiled(uid):
        from_profile
     except NameError:
         from tiled.client import from_profile
-        tiled_client = from_profile("xpd-ldrd20-31")  
+        # tiled_client = from_profile("xpd-ldrd20-31")
+        tiled_client = from_profile("xpd")  
 
     run = tiled_client[uid]
     ds = run.primary.read()
@@ -485,7 +521,8 @@ def read_qepro_from_db(uid):
         db
     except NameError:
         import databroker
-        db = databroker.Broker.named('xpd-ldrd20-31')
+        # db = databroker.Broker.named('xpd-ldrd20-31')
+        db = databroker.Broker.named('xpd')
 
     # full_uid = db[uid].start['uid']
     # unix_time = db[uid].start['time']     

scripts/_plot_helper.py

@@ -163,7 +163,7 @@ def plot_average_good(self, x, y, color=None, label=None, clf_limit=10):
 
 
 
-    def plot_CsPbX3(self, x, y, wavelength, wavelength_range=[420, 660], label=None, clf_limit=10):        
+    def plot_CsPbX3(self, x, y, wavelength, label=None, clf_limit=10):        
 
         # import palettable.colorbrewer.diverging as pld
         # palette = pld.RdYlGn_4_r
@@ -273,7 +273,7 @@ def plot_fitting(self, x, y, popt_list, single_f, fill_between=True, num_var=3):
 
 
 
-def color_idx_map_halides(peak_wavelength, halide_w_range=[420, 520, 660]):
+def color_idx_map_halides(peak_wavelength, halide_w_range=[400, 520, 660]):
 
     from matplotlib.colors import LinearSegmentedColormap
     colors = [

scripts/kafka_consumer_iterate_qserver.py

@@ -116,11 +116,15 @@
 
 use_good_bad = True
 USE_AGENT_iterate = True
-peak_target = 580
+peak_target = 590
 
 write_agent_data = True
 # rate_label = ['infusion_rate_CsPb', 'infusion_rate_Br', 'infusion_rate_Cl', 'infusion_rate_I2']
-rate_label = ['infusion_rate_CsPb', 'infusion_rate_Br', 'infusion_rate_I2', 'infusion_rate_Cl']
+# rate_label = ['infusion_rate_CsPb', 'infusion_rate_Br', 'infusion_rate_I2', 'infusion_rate_Cl']
+rate_label_dic =   {'CsPb':'infusion_rate_CsPb', 
+                    'Br':'infusion_rate_Br', 
+                    'ZnI':'infusion_rate_I2', 
+                    'ZnCl':'infusion_rate_Cl'}
 
 if USE_AGENT_iterate:
 
@@ -166,7 +170,7 @@ def print_kafka_messages(beamline_acronym, csv_path=csv_path,
                          pump_list=pump_list, sample=sample, precursor_list=precursor_list, 
                          mixer=mixer, dummy_test=dummy_kafka, plqy=PLQY, prefix=prefix, 
                          agent_data_path=agent_data_path, peak_target=peak_target, 
-                         rate_label=rate_label):
+                         rate_label_dic=rate_label_dic):
 
     print(f"Listening for Kafka messages for {beamline_acronym}")
     print(f'Defaul parameters:\n'
@@ -373,30 +377,15 @@ def print_message(consumer, doctype, doc,
                             plqy_dic = {'PL_integral':PL_integral_s, 'Absorbance_365':absorbance_s, 'plqy': plqy}
 
                             optical_property = {'Peak': peak_emission, 'FWHM':fwhm, 'PLQY':plqy}
-
-
-                            ## Check if pump is Infusing or Idle
-                            is_infusing = []
-                            for pump_status in metadata_dic['pump_status']:
-                                if pump_status == 'Infusing':
-                                    is_infusing.append(1)
-                                elif pump_status == 'Idle':
-                                    is_infusing.append(0)
 
                             if write_agent_data:
                                 agent_data = {}
 
                                 agent_data.update(optical_property)
                                 agent_data.update({k:v for k, v in metadata_dic.items() if len(np.atleast_1d(v)) == 1})
-                                # agent_data.update({k:v for k, v in metadata_dic.items()})
-
-                                for i in range(len(rate_label)):
-                                    try:
-                                        ruc = da.rate_unit_converter(r0 = metadata_dic["infuse_rate_unit"][i], r1 = 'ul/min')
-                                        agent_data[rate_label[i]] = metadata_dic["infuse_rate"][i]*ruc*is_infusing[i]
-                                    except IndexError:
-                                        agent_data[rate_label[i]] = 0.0
 
+                                agent_data = de._exprot_rate_agent(metadata_dic, rate_label_dic, agent_data)
+
                                 with open(f"{agent_data_path}/{data_id}.json", "w") as f:
                                     json.dump(agent_data, f)
 

scripts/kafka_consumer_publisher.py

@@ -21,7 +21,7 @@
 from _plot_helper import plot_uvvis
 import _data_analysis as da
 
-from bluesky_queueserver.manager.comms import zmq_single_request
+# from bluesky_queueserver.manager.comms import zmq_single_request
 
 # db = databroker.Broker.named('xpd-ldrd20-31')
 # catalog = databroker.catalog['xpd-ldrd20-31']
@@ -39,7 +39,8 @@
 
 ## Input varaibales: read from inputs_qserver_kafka.xlsx
 xlsx = '/home/xf28id2/Documents/ChengHung/inputs_kafka_single.xlsx'
-input_dic = de._read_input_xlsx(xlsx)
+input_dic = de._read_input_xlsx(xlsx, sheet_name='inputs_quinine')
+# input_dic = de._read_input_xlsx(xlsx, sheet_name='inputs')
 
 ##################################################################
 # Define namespace for tasks in Qserver and Kafa
@@ -73,13 +74,16 @@
 
 write_agent_data = True
 # rate_label = ['infusion_rate_CsPb', 'infusion_rate_Br', 'infusion_rate_Cl', 'infusion_rate_I2']
-rate_label = ['infusion_rate_CsPb', 'infusion_rate_Br', 'infusion_rate_I2', 'infusion_rate_Cl']
+rate_label_dic =   {'CsPb':'infusion_rate_CsPb', 
+                    'Br':'infusion_rate_Br', 
+                    'ZnI':'infusion_rate_I2', 
+                    'ZnCl':'infusion_rate_Cl'}
 
 
 def print_kafka_messages(beamline_acronym, csv_path=csv_path, 
                          key_height=key_height, height=height, distance=distance,  
                          dummy_test=dummy_kafka, plqy=PLQY, 
-                         agent_data_path=agent_data_path, rate_label=rate_label):
+                         agent_data_path=agent_data_path, rate_label_dic=rate_label_dic):
 
     print(f"Listening for Kafka messages for {beamline_acronym}")
     print(f'Defaul parameters:\n'
@@ -286,30 +290,15 @@ def print_message(consumer, doctype, doc,
                             plqy_dic = {'PL_integral':PL_integral_s, 'Absorbance_365':absorbance_s, 'plqy': plqy}
 
                             optical_property = {'Peak': peak_emission, 'FWHM':fwhm, 'PLQY':plqy}
-
-
-                            ## Check if pump is Infusing or Idle
-                            is_infusing = []
-                            for pump_status in metadata_dic['pump_status']:
-                                if pump_status == 'Infusing':
-                                    is_infusing.append(1)
-                                elif pump_status == 'Idle':
-                                    is_infusing.append(0)
 
                             if write_agent_data:
                                 agent_data = {}
 
                                 agent_data.update(optical_property)
                                 agent_data.update({k:v for k, v in metadata_dic.items() if len(np.atleast_1d(v)) == 1})
-                                # agent_data.update({k:v for k, v in metadata_dic.items()})
-
-                                for i in range(len(rate_label)):
-                                    try:
-                                        ruc = da.rate_unit_converter(r0 = metadata_dic["infuse_rate_unit"][i], r1 = 'ul/min')
-                                        agent_data[rate_label[i]] = metadata_dic["infuse_rate"][i]*ruc*is_infusing[i]
-                                    except IndexError:
-                                        agent_data[rate_label[i]] = 0.0
 
+                                agent_data = de._exprot_rate_agent(metadata_dic, rate_label_dic, agent_data)
+
                                 with open(f"{agent_data_path}/{data_id}.json", "w") as f:
                                     json.dump(agent_data, f)
 

scripts/prepare_agent.py

@@ -16,11 +16,11 @@ def build_agen(peak_target=660, peak_tolerance=5):
     agent_data_path = '/home/xf28id2/data_halide'
 
 
-    if peak_target > 525:
+    if peak_target > 515:
         I_up_limit = 200
         Cl_up_limit = 0
 
-    elif peak_target < 515:
+    elif peak_target < 500:
         I_up_limit = 0
         Cl_up_limit = 200
 
@@ -79,7 +79,7 @@ def build_agen(peak_target=660, peak_tolerance=5):
         with open(fp, "r") as f:
             data = json.load(f)
 
-        r_2_min = 0.85
+        r_2_min = 0.05
         try: 
             if data['r_2'] < r_2_min:
                 print(f'Skip because "r_2" of {os.path.basename(fp)} is {data["r_2"]:.2f} < {r_2_min}.')

scripts/prepare_agent_select.py

@@ -0,0 +1,115 @@
+
+import os
+import json
+import glob
+import sys
+import numpy as np
+from tqdm import tqdm
+# sys.path.insert(0, "/home/xf28id2/src/blop")
+
+from blop import Agent, DOF, Objective
+
+
+def build_agen2(peak_target=660, peak_tolerance=5):
+    # data_path = '/home/xf28id2/data_ZnCl2'
+    #data_path = '/home/xf28id2/data'
+    agent_data_path = '/home/xf28id2/data_halide'
+
+
+    if peak_target > 515:
+        I_up_limit = 200
+        Cl_up_limit = 0
+        CsPbX3 = 'CsPbI3'
+
+    elif peak_target < 500:
+        I_up_limit = 0
+        Cl_up_limit = 200
+        CsPbX3 = 'CsPbCl3'
+
+    else:
+        I_up_limit = 0
+        Cl_up_limit = 0
+        CsPbX3 = 'CsPbBr3'
+
+
+    # dofs = [
+    #     DOF(description="CsPb(oleate)3", name="infusion_rate_CsPb", units="uL/min", search_bounds=(8, 110)),
+    #     DOF(description="TOABr", name="infusion_rate_Br", units="uL/min", search_bounds=(50, 200)),
+    #     DOF(description="ZnCl2", name="infusion_rate_Cl", units="uL/min", search_bounds=(0, Cl_up_limit)),
+    #     DOF(description="ZnI2", name="infusion_rate_I2", units="uL/min", search_bounds=(0, I_up_limit)),
+    # ]
+
+
+    dofs = [
+        DOF(description="CsPb(oleate)3", name="infusion_rate_CsPb", units="uL/min", search_bounds=(8, 110)),
+        DOF(description="TOABr", name="infusion_rate_Br", units="uL/min", search_bounds=(50, 200)),
+        DOF(description="ZnI2", name="infusion_rate_I2", units="uL/min", search_bounds=(0, I_up_limit)), 
+        DOF(description="ZnCl2", name="infusion_rate_Cl", units="uL/min", search_bounds=(0, Cl_up_limit)),
+    ]  
+
+
+    objectives = [
+        Objective(description="Peak emission", name="Peak", target=(peak_target-peak_tolerance, peak_target+peak_tolerance), weight=1, max_noise=0.25),
+        Objective(description="Peak width", name="FWHM", target="min", log=True, weight=1., max_noise=0.25),
+        Objective(description="Quantum yield", name="PLQY", target="max", log=True, weight=1., max_noise=0.25),
+    ]
+
+
+
+
+    # objectives = [
+    #     Objective(name="Peak emission", key="peak_emission", target=525, units="nm"),
+    #     Objective(name="Peak width", key="peak_fwhm", minimize=True, units="nm"),
+    #     Objective(name="Quantum yield", key="plqy"),
+    # ]
+
+    USE_AGENT = False
+
+    agent = Agent(dofs=dofs, objectives=objectives, db=None, verbose=True)
+    #agent.load_data("~/blop/data/init.h5")
+
+    metadata_keys = ["time", "uid", "r_2"]
+
+    init_file = "/home/xf28id2/data_halide/init_240122_01.h5"
+
+    # if os.path.exists(init_file):
+    #     agent.load_data(init_file)
+
+    # else:
+    filepaths = glob.glob(f"{agent_data_path}/*.json")
+    filepaths.sort()
+    for fp in tqdm(filepaths):
+        with open(fp, "r") as f:
+            data = json.load(f)
+
+        _filter = { 'CsPbBr3':data['infusion_rate_I2'] == 0 and data['infusion_rate_Cl'] == 0,
+                    'CsPbI3':data['infusion_rate_I2'] != 0 and data['infusion_rate_Cl'] == 0,
+                    'CsPbCl3':data['infusion_rate_I2'] == 0 and data['infusion_rate_Cl'] != 0,}
+
+        if _filter[CsPbX3]:
+
+            r_2_min = 0.05
+            try: 
+                if data['r_2'] < r_2_min:
+                    print(f'Skip because "r_2" of {os.path.basename(fp)} is {data["r_2"]:.2f} < {r_2_min}.')
+                else: 
+                    x = {k:[data[k]] for k in agent.dofs.names}
+                    y = {k:[data[k]] for k in agent.objectives.names}
+                    metadata = {k:[data.get(k, None)] for k in metadata_keys}
+                    agent.tell(x=x, y=y, metadata=metadata, train=False, update_models=False)
+
+            except (KeyError):
+                print(f'{os.path.basename(fp)} has no "r_2".')
+
+        else:
+            pass
+
+    agent._construct_all_models()
+    agent._train_all_models()
+
+    print(f'The target of the emission peak is {peak_target} nm.')
+
+    return agent
+
+    # print(agent.ask("qei", n=1))
+    # print(agent.ask("qr", n=36))