sabiharustam /
voltcycle
| @@ 78-123 (lines=46) @@ | ||
| 75 | return dict_of_df, number |
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| 76 | ||
| 77 | ||
| 78 | def read_file(file): |
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| 79 | """This function reads the raw data file, gets the scanrate and stepsize |
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| 80 | and then reads the lines according to cycle number. Once it reads the data |
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| 81 | for one cycle, it calls read_cycle function to denerate a dataframe. It |
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| 82 | does the same thing for all the cycles and finally returns a dictionary, |
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| 83 | the keys of which are the cycle numbers and the values are the |
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| 84 | corresponding dataframes. |
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| 85 | ||
| 86 | Parameters |
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| 87 | __________ |
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| 88 | file: raw data file |
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| 89 | ||
| 90 | Returns: |
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| 91 | ________ |
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| 92 | dict_of_df: dictionary of dataframes with keys = cycle numbers and |
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| 93 | values = dataframes for each cycle |
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| 94 | n_cycle: number of cycles in the raw file |
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| 95 | """ |
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| 96 | dict_of_df = {} |
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| 97 | h_val = 0 |
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| 98 | l_val = 0 |
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| 99 | n_cycle = 0 |
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| 100 | #a = [] |
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| 101 | with open(file, 'rt') as f_val: |
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| 102 | print(file + ' Opened') |
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| 103 | for line in f_val: |
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| 104 | record = 0 |
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| 105 | if not (h_val and l_val): |
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| 106 | if line.startswith('SCANRATE'): |
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| 107 | scan_rate = float(line.split()[2]) |
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| 108 | h_val = 1 |
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| 109 | if line.startswith('STEPSIZE'): |
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| 110 | step_size = float(line.split()[2]) |
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| 111 | l_val = 1 |
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| 112 | if line.startswith('CURVE'): |
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| 113 | n_cycle += 1 |
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| 114 | if n_cycle > 1: |
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| 115 | number = n_cycle - 1 |
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| 116 | data = read_cycle(a_val) |
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| 117 | key_name = 'cycle_' + str(number) |
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| 118 | #key_name = number |
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| 119 | dict_of_df[key_name] = copy.deepcopy(data) |
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| 120 | a_val = [] |
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| 121 | if n_cycle: |
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| 122 | a_val.append(line) |
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| 123 | return dict_of_df, number |
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| 124 | ||
| 125 | #df = pd.DataFrame(list(dict1['df_1'].items())) |
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| 126 | #list1, list2 = list(dict1['df_1'].items()) |
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| @@ 30-75 (lines=46) @@ | ||
| 27 | return dataframe |
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| 28 | ||
| 29 | ||
| 30 | def read_file(file): |
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| 31 | """This function reads the raw data file, gets the scanrate and stepsize |
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| 32 | and then reads the lines according to cycle number. Once it reads the data |
|
| 33 | for one cycle, it calls read_cycle function to denerate a dataframe. It |
|
| 34 | does the same thing for all the cycles and finally returns a dictionary, |
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| 35 | the keys of which are the cycle numbers and the values are the |
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| 36 | corresponding dataframes. |
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| 37 | ||
| 38 | Parameters |
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| 39 | __________ |
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| 40 | file: raw data file |
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| 41 | ||
| 42 | Returns: |
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| 43 | ________ |
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| 44 | dict_of_df: dictionary of dataframes with keys = cycle numbers and |
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| 45 | values = dataframes for each cycle |
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| 46 | n_cycle: number of cycles in the raw file |
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| 47 | """ |
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| 48 | dict_of_df = {} |
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| 49 | h_val = 0 |
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| 50 | l_val = 0 |
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| 51 | n_cycle = 0 |
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| 52 | #a = [] |
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| 53 | with open(file, 'rt') as f_val: |
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| 54 | print(file + ' Opened') |
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| 55 | for line in f_val: |
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| 56 | record = 0 |
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| 57 | if not (h_val and l_val): |
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| 58 | if line.startswith('SCANRATE'): |
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| 59 | scan_rate = float(line.split()[2]) |
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| 60 | h_val = 1 |
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| 61 | if line.startswith('STEPSIZE'): |
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| 62 | step_size = float(line.split()[2]) |
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| 63 | l_val = 1 |
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| 64 | if line.startswith('CURVE'): |
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| 65 | n_cycle += 1 |
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| 66 | if n_cycle > 1: |
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| 67 | number = n_cycle - 1 |
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| 68 | data = read_cycle(a_val) |
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| 69 | key_name = 'cycle_' + str(number) |
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| 70 | #key_name = number |
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| 71 | dict_of_df[key_name] = copy.deepcopy(data) |
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| 72 | a_val = [] |
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| 73 | if n_cycle: |
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| 74 | a_val.append(line) |
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| 75 | return dict_of_df, number |
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| 76 | ||
| 77 | ||
| 78 | #df = pd.DataFrame(list(dict1['df_1'].items())) |
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