pqcprep.file_tools

  1"""
  2Collection of functions relating to file handling. 
  3"""
  4
  5import os 
  6from fractions import Fraction
  7
  8def compress_args(n,m,L, seed, epochs,func_str,loss_str,meta,nint, mint, phase_reduce, train_superpos, real, repeat_params, WILL_p, WILL_q, delta):
  9    """
 10    Compress a set of variables used for training the function evaluation network into a dictionary . 
 11
 12    Arguments:
 13    ---- 
 14
 15    Arguments are the same as those passed to `.training_tools.train_QNN()`. See the description there.
 16
 17
 18    Returns:
 19    ---
 20
 21    - **arg_dict** : *dict* 
 22
 23        Dictionary containing variable values 
 24
 25    """
 26    arg_dict = {}
 27    arg_dict["n"]=n 
 28    arg_dict["m"]=m
 29    arg_dict["L"]=L
 30    arg_dict["seed"]=seed
 31    arg_dict["epochs"]=epochs 
 32    arg_dict["func_str"]=func_str
 33    arg_dict["loss_str"]=loss_str
 34    arg_dict["meta"]=meta
 35    arg_dict["nint"]=nint
 36    arg_dict["mint"]=mint
 37    arg_dict["phase_reduce"]=phase_reduce
 38    arg_dict["train_superpos"]=train_superpos
 39    arg_dict["real"]=real
 40    arg_dict["repeat_params"]=repeat_params
 41    arg_dict["WILL_p"]=WILL_q
 42    arg_dict["WILL_q"]=WILL_p
 43    arg_dict["delta"]=delta
 44
 45    return arg_dict
 46
 47def compress_args_ampl(n,L,x_min,x_max,seed, epochs,func_str,loss_str,meta, nint, repeat_params):
 48    """
 49    Compress a set of variables used for training the amplitude encoding network into a dictionary . 
 50
 51    Arguments:
 52    ---- 
 53
 54    Arguments are the same as those passed to `.training_tools.ampl_train_QNN()`. See the description there. 
 55
 56
 57    Returns:
 58    ----
 59
 60    - **arg_dict** : *dict* 
 61
 62        Dictionary containing variable values 
 63
 64    """
 65    arg_dict = {}
 66    arg_dict["n"]=n 
 67    arg_dict["L"]=L
 68    arg_dict["seed"]=seed
 69    arg_dict["epochs"]=epochs 
 70    arg_dict["func_str"]=func_str
 71    arg_dict["loss_str"]=loss_str
 72    arg_dict["meta"]=meta
 73    arg_dict["nint"]=nint
 74    arg_dict["x_min"]=x_min
 75    arg_dict["x_max"]=x_max
 76    arg_dict["repeat_params"]=repeat_params
 77    
 78    return arg_dict
 79
 80def vars_to_name_str(arg_dict):
 81    """
 82    Generate a name string from a set of variables used for training the function evaluation network.
 83
 84    Arguments:
 85    ---- 
 86
 87    - **arg_dict** : *dict* 
 88
 89        Dictionary created from variable values using `compress_args()`. 
 90
 91
 92    Returns:
 93    ---
 94    - **name_str** : *str* 
 95
 96        Name string in the appropriate file naming convention. 
 97    
 98    """
 99    # set precision information 
100    if arg_dict["nint"]==None or arg_dict["nint"]==arg_dict["n"]:
101        arg_dict["nis"]=""
102    else:
103        arg_dict["nis"]=f'({arg_dict["nint"]})'
104    if arg_dict["mint"]==None or arg_dict["mint"]==arg_dict["m"]:
105        arg_dict["mis"]=""
106    else:
107        arg_dict["mis"]=f'({arg_dict["mint"]})' 
108
109    # add to meta string
110    if arg_dict["train_superpos"] and '(S)' not in arg_dict["meta"]:
111        arg_dict["meta"]+='(S)'
112    if arg_dict["phase_reduce"] and '(PR)' not in arg_dict["meta"]:
113        arg_dict["mis"]="(0)"
114        arg_dict["meta"]+='(PR)' 
115    if arg_dict["phase_reduce"]:
116        arg_dict["mis"]="(0)"    
117    if arg_dict["real"] and '(r)' not in arg_dict["meta"]:
118        arg_dict["meta"]+='(r)' 
119    if arg_dict["repeat_params"] != None and f'({arg_dict["repeat_params"]})' not in arg_dict["meta"]:
120        arg_dict["meta"]+=f'({arg_dict["repeat_params"]})' 
121
122    # set WILL information   
123    if arg_dict["loss_str"] !="WILL" and '--' in arg_dict["meta"]:
124        raise ValueError("The sequence '--' is reserved and may not appear in the meta string.")    
125
126    if arg_dict["loss_str"]=="WILL" and '--' not in arg_dict["meta"]:
127            arg_dict["meta"] +=f'_{Fraction(arg_dict["WILL_p"]).numerator}--{Fraction(arg_dict["WILL_p"]).denominator}_{Fraction(arg_dict["WILL_q"]).numerator}--{Fraction(arg_dict["WILL_q"]).denominator}'
128
129    # set name string 
130    name_str = f'_{arg_dict["n"]}{arg_dict["nis"]}_{arg_dict["m"]}{arg_dict["mis"]}_{arg_dict["L"]}_{arg_dict["epochs"]}_{arg_dict["func_str"]}_{arg_dict["loss_str"]}_{arg_dict["delta"]}_{arg_dict["meta"]}_{arg_dict["seed"]}' 
131
132    return name_str       
133
134def vars_to_name_str_ampl(arg_dict):
135    """
136    Generate a name string from a set of variable used for training the amplitude encoding network.
137
138    Arguments:
139    ---- 
140    - **arg_dict** : *dict* 
141
142        Dictionary created from variable values using `compress_args_ampl()`. 
143
144
145    Returns:
146    ----
147    - **name_str** : *str* 
148
149        Name string in the appropriate file naming convention. 
150    
151    """
152    # set precision information 
153    if arg_dict["nint"]==None or  arg_dict["nint"]== arg_dict["n"]:
154        arg_dict["nis"]=""
155    else:
156        arg_dict["nis"]=f'({arg_dict["nint"]})'
157     
158    # add to meta string
159    if  arg_dict["repeat_params"] and '(RP)' not in arg_dict["meta"]:
160        arg_dict["meta"]+='(RP)' 
161
162    # set name string 
163    name_str = f'_{arg_dict["n"]}{arg_dict["nis"]}_{arg_dict["L"]}_{arg_dict["epochs"]}_{arg_dict["func_str"]}_{arg_dict["loss_str"]}_{arg_dict["x_min"]}_{arg_dict["x_max"]}_{arg_dict["meta"]}_{arg_dict["seed"]}'
164
165    return name_str 
166
167def check_duplicates(arg_dict,DIR, ampl=False):
168    """
169    For a given set of input parameters, check if training and testing results already exist. 
170
171    Arguments:
172    ---- 
173
174    - **arg_dict** : *dict* 
175
176        Dictionary created from variable values using `compress_args()` or `compress_args_ampl()`. 
177    
178    - **DIR** : *str* 
179
180        Parent directory for output files.       
181
182    - **ampl** : *boolean* 
183
184        If True, check if results exist for the amplitude-only network. Default is False     
185        
186
187    Returns:
188    ---
189    - **exist** : *boolean* 
190
191        Returns True if results already exists and False otherwise. 
192    
193    """
194    if ampl:
195        name=vars_to_name_str_ampl(arg_dict)
196        out_dir="ampl_outputs"
197        labels=["mismatch", "loss", "weights", "statevec"]   
198    else:                 
199        name=vars_to_name_str(arg_dict)
200        out_dir="outputs"
201        labels=["mismatch", "loss", "weights", "grad", "vargrad"]  
202
203    count=0 
204
205    for i in range(len(labels)):
206        if os.path.isfile(os.path.join(DIR,out_dir, f"{labels[i]}{name}.npy")):
207            count +=1
208
209    return count==len(labels)
210    
211def check_temp(arg_dict,DIR, ampl=False): 
212    """
213    For a given set of input parameters, check if temp files already exist. 
214
215    Arguments:
216    ---- 
217
218    - **arg_dict** : *dict* 
219
220        Dictionary created from variable values using `compress_args()` or `compress_args_ampl()`. 
221
222    - **DIR** : *str* 
223
224        Parent directory for output files.       
225
226    - **ampl** : *boolean* 
227
228        If True, check if results exist for the amplitude-only network. Default is False   
229
230
231    Returns:
232    ---
233    - **exist** : *boolean* 
234
235        Returns True if results already exist and False otherwise. 
236    
237    """  
238    
239    # set precision strings 
240    if ampl:
241        name=vars_to_name_str_ampl(arg_dict)
242        out_dir="ampl_outputs"
243        labels=["mismatch", "loss", "weights", "statevec"]   
244    else:                 
245        name=vars_to_name_str(arg_dict)
246        out_dir="outputs"
247        labels=["mismatch", "loss", "weights", "grad", "vargrad"]  
248
249    count=0 
250
251    for k in range(100,arg_dict["epochs"], step=100):    
252        for i in range(len(labels)):
253            if os.path.isfile(os.path.join(DIR,out_dir, f"__TEMP{k}_{labels[i]}{name}.npy")):
254                count +=1
255
256    return count==len(labels)
257
258
259def check_plots(arg_dict,DIR):  
260    """
261    For a given set of input parameters, check if plots already exist (excluding compare plots). 
262
263    Arguments:
264    ---- 
265
266    - **arg_dict** : *dict* 
267
268        Dictionary created from variable values using `compress_args()` or `compress_args_ampl()`. 
269
270    - **DIR** : *str* 
271
272        Parent directory for output files.          
273    
274    Returns:
275    ---
276    - **exist** : *boolean* 
277
278        Returns True if results already exist and False otherwise. 
279    
280    """
281    name=vars_to_name_str(arg_dict)  
282    out_dir="plots"          
283    labels=["mismatch", "loss", "bar_mismatch"]
284
285    count=0 
286
287    for i in range(len(labels)):
288        if os.path.isfile(os.path.join(DIR, out_dir, f"{labels[i]}{name}.npy")):
289            count +=1
290
291    return count==len(labels)