I have a csv file with following structure:
Tokens,Tags,Polarities
"['i', 'agree', 'about', 'arafat', '.', 'i', 'mean', ',', 'shit', ',', 'they', 'even', 'gave', 'one', 'to', 'jimmy', 'carter', 'ha', '.', 'it', 'should', 'be', 'called', ""''"", 'the', 'worst', 'president', ""''"", 'prize', '.']","[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]"
"['musicmonday', 'britney', 'spears', '-', 'lucky', 'do', 'you', 'remember', 'this', 'song', '?', 'it', '`', 's', 'awesome', '.', 'i', 'love', 'it', '.']","[0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[-1, 2, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]"
"['wtf', '?', 'hilary', 'swank', 'is', 'coming', 'to', 'my', 'school', 'today', ',', 'just', 'to', 'chill', '.', 'lol', 'wow']","[0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[-1, -1, 1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]"
"['my', '3-year-old', 'was', 'amazed', 'yesterday', 'to', 'find', 'that', ""'"", 'real', ""'"", '10', 'pin', 'bowling', 'is', 'nothing', 'like', 'it', 'is', 'on', 'the', 'wii', '...']","[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]","[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, -1]"
"['God', 'damn', '.', 'That', 'Sony', 'remote', 'for', 'google', 'is', 'fucking', 'hideeeeeous', '!']","[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]","[-1, -1, -1, -1, -1, -1, -1, 0, -1, -1, -1, -1]"
I am trying to read the file as follows:
twitter_train = pd.read_csv('twitter_train.csv')
Then I can see that it has a correct structure:
twitter_train.head(3)
Tokens Tags Polarities
0 ['i', 'agree', 'about', 'arafat', '.', 'i', 'm... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -...
1 ['musicmonday', 'britney', 'spears', '-', 'luc... [0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [-1, 2, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1,...
2 ['wtf', '?', 'hilary', 'swank', 'is', 'coming'... [0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [-1, -1, 1, 1, -1, -1, -1, -1, -1, -1, -1, -1,...
I want to convert each column to a list of lists, for example:
twitter_train_lists = twitter_train['Tokens'].tolist()
But I have incorrect structure that has an extra \ or " with each element in the list and around each list itself:
['[\'i\', \'agree\', \'about\', \'arafat\', \'.\', \'i\', \'mean\', \',\', \'shit\', \',\', \'they\', \'even\', \'gave\', \'one\', \'to\', \'jimmy\', \'carter\', \'ha\', \'.\', \'it\', \'should\', \'be\', \'called\', "\'\'", \'the\', \'worst\', \'president\', "\'\'", \'prize\', \'.\']',
"['musicmonday', 'britney', 'spears', '-', 'lucky', 'do', 'you', 'remember', 'this', 'song', '?', 'it', '`', 's', 'awesome', '.', 'i', 'love', 'it', '.']",
"['wtf', '?', 'hilary', 'swank', 'is', 'coming', 'to', 'my', 'school', 'today', ',', 'just', 'to', 'chill', '.', 'lol', 'wow']",
'[\'my\', \'3-year-old\', \'was\', \'amazed\', \'yesterday\', \'to\', \'find\', \'that\', "\'", \'real\', "\'", \'10\', \'pin\', \'bowling\', \'is\', \'nothing\', \'like\', \'it\', \'is\', \'on\', \'the\', \'wii\', \'...\']',
"['God', 'damn', '.', 'That', 'Sony', 'remote', 'for', 'google', 'is', 'fucking', 'hideeeeeous', '!']"]
How I can extract lists properly from this csv file to get the correct structure:
[['i', 'agree', 'about', 'arafat', '.', 'i', 'mean', ',', 'shit', ',', 'they', 'even', 'gave', 'one', 'to', 'jimmy', 'carter', 'ha', '.', 'it', 'should', 'be', 'called', "''", 'the', 'worst', 'president', "''", 'prize', '.'],
['musicmonday', 'britney', 'spears', '-', 'lucky', 'do', 'you', 'remember', 'this', 'song', '?', 'it', '`', 's', 'awesome', '.', 'i', 'love', 'it', '.'],
['wtf', '?', 'hilary', 'swank', 'is', 'coming', 'to', 'my', 'school', 'today', ',', 'just', 'to', 'chill', '.', 'lol', 'wow'],
['my', '3-year-old', 'was', 'amazed', 'yesterday', 'to', 'find', 'that', "'", 'real', "'", '10', 'pin', 'bowling', 'is', 'nothing', 'like', 'it', 'is', 'on', 'the', 'wii', '...'],
['God', 'damn', '.', 'That', 'Sony', 'remote', 'for', 'google', 'is', 'fucking', 'hideeeeeous', '!']]
You can find the original dataset file here: https://github.com/1tangerine1day/Aspect-Term-Extraction-and-Analysis/tree/master/data
Update:
I tried another way but have the same problem:
import csv
with open('twitter_train.csv', newline='') as f:
reader = csv.reader(f)
data = list(reader)
Another incorrect output:
print(data[3])
["['wtf', '?', 'hilary', 'swank', 'is', 'coming', 'to', 'my', 'school', 'today', ',', 'just', 'to', 'chill', '.', 'lol', 'wow']", '[0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]', '[-1, -1, 1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]']
Thanks in advance!
Your info in your csv is actually a string not a list. You need to make them actual lists.
twitter_train = pd.read_csv('twitter_train.csv')
twitter_train['Tokens'] = list(twitter_train['Tokens'].str.strip("['").str.rstrip("']").str.split("', '"))
Related
I am having some issues with GNUradio when trying to use OFDM transmitter and reciever. I am vaguely following the example on the WiKi here is my flow chart:
I am struggling to get the correct values for the OFDM modules. I have tried multiple values for the Occupied Carriers and Sync Word.
Current values:
Occupied Carriers: (list(range(-26, -21)) + list(range(-20, -7)) + list(range(-6, 0)) + list(range(1, 7)) + list(range(8, 21)) + list(range(22, 27)),)
Sync Word 1&2: (0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Pilot Carrier: ((-21, -7, 7, 21,),)
This program is not working and it gives the following error:
TypeError: __init__(): incompatible constructor arguments. The following argument types are supported:
1. gnuradio.digital.digital_python.ofdm_carrier_allocator_cvc(fft_len: int, occupied_carriers: List[List[int]], pilot_carriers: List[List[int]], pilot_symbols: List[List[complex]], sync_words: List[List[complex]], len_tag_key: str = 'packet_len', output_is_shifted: bool = True)
Invoked with: 64; kwargs: occupied_carriers=([-26, -25, -24, -23, -22, -20, -19, -18, -17, -16, -15, -14, -13, -12, -11, -10, -9, -8, -6, -5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26],), pilot_carriers=((-21, -7, 7, 21),), pilot_symbols=(1, 1, 1, -1), sync_words=[(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], len_tag_key='packet_len'
This is new territory for me so please ask if I can make any clarifications
when I order with an obligation to pay
[PrestaShopException]
Can't load Order status
at line 242 in file classes/PaymentModule.php
237. }
238.
239. $order_status = new OrderState((int) $id_order_state, (int) $this->context->language->id);
240. if (!Validate::isLoadedObject($order_status)) {
241. PrestaShopLogger::addLog('PaymentModule::validateOrder - Order Status cannot be loaded', 3, null, 'Cart', (int) $id_cart, true);
242. throw new PrestaShopException('Can\'t load Order status');
243. }
244.
245. if (!$this->active) {
246. PrestaShopLogger::addLog('PaymentModule::validateOrder - Module is not active', 3, null, 'Cart', (int) $id_cart, true);
247. die(Tools::displayError());
PaymentModuleCore->validateOrder - [line 58 - modules/ps_wirepayment/controllers/front/validation.php] - [9 Arguments]
Ps_WirepaymentValidationModuleFrontController->postProcess - [line 270 - classes/controller/Controller.php]
ControllerCore->run - [line 509 - classes/Dispatcher.php]
DispatcherCore->dispatch - [line 24 - override/classes/Dispatcher.php]
Dispatcher->dispatch - [line 28 - index.php]
insert with phpmyadmin the table
ps_order_state:
INSERT INTO `ps_order_state` (`id_order_state`, `invoice`, `send_email`, `module_name`, `color`, `unremovable`, `hidden`, `logable`, `delivery`, `shipped`, `paid`, `deleted`) VALUES
(1, 0, 1, 'cheque', 'RoyalBlue', 1, 0, 0, 0, 0, 0, 0),
(2, 1, 1, '', 'LimeGreen', 1, 0, 1, 0, 0, 1, 0),
(3, 1, 1, '', 'DarkOrange', 1, 0, 1, 0, 0, 1, 0),
(4, 1, 1, '', 'BlueViolet', 1, 0, 1, 1, 1, 1, 0),
(5, 1, 0, '', '#108510', 1, 0, 1, 1, 1, 1, 0),
(6, 0, 1, '', 'Crimson', 1, 0, 0, 0, 0, 0, 0),
(7, 1, 1, '', '#ec2e15', 1, 0, 0, 0, 0, 0, 0),
(8, 0, 1, '', '#8f0621', 1, 0, 0, 0, 0, 0, 0),
(9, 1, 1, '', 'HotPink', 1, 0, 0, 0, 0, 1, 0),
(10, 0, 1, 'bankwire', 'RoyalBlue', 1, 0, 0, 0, 0, 0, 0),
(11, 0, 0, '', 'RoyalBlue', 1, 0, 0, 0, 0, 0, 0),
(12, 1, 1, '', 'LimeGreen', 1, 0, 1, 0, 0, 1, 0),
(13, 1, 0, '', '#DDEEFF', 0, 0, 1, 0, 0, 0, 0);
Your ps_wirepayment module is trying to set an order status to an invalid / deleted id_order_state.
If you haven't modified it , bankwire modulee relies on ps_configuration "PS_OS_BANKWIRE" status value,
so make sure the ID value is valid and links to a valid order state in your database.
I ran a LIWC analysis and it gives me the following results (below). I would like to turn the result into a pandas dataframe. If anyone can chip in, that would be wonderful.
Thanks in advance :)
Best,
David
resp = requests.post(url, auth=(api_key, api_secret), data=data)
resp1 = resp
print(resp.json())
{'plan_usage': {'call_limit': 1000, 'calls_made': 6, 'calls_remaining': 994, 'percent_used': 0.6, 'start_date': '2020-12-09T03:05:57.779556Z', 'end_date': '2020-12-23T03:05:57.779556Z'}, 'results': [{'response_id': 'd1382f42-5c28-4528-ab2e-81b80ba185e2', 'request_id': 'req-1', 'language': 'en', 'version': 'v1.0.0', 'summary': {'word_count': 57, 'words_per_sentence': 11.4, 'sentence_count': 5, 'six_plus_words': 0.2982456140350877, 'emojis': 0, 'emoticons': 0, 'hashtags': 0, 'urls': 0}, 'liwc': {'scores': {'analytical_thinking': 80.77394876079086, 'authentic': 38.8220872694557, 'clout': 50, 'emotional_tone': 97.58138119866139, 'dictionary_words': 0.8771929824561403, 'categories': {'achievement': 0, 'adjectives': 0.017543859649122806, 'adverbs': 0.03508771929824561, 'affect': 0.05263157894736842, 'affiliation': 0.017543859649122806, 'all_punctuation': 0.10526315789473684, 'anger_words': 0, 'anxiety_words': 0, 'apostrophes': 0, 'articles': 0.12280701754385964, 'assent': 0, 'auxiliary_verbs': 0.14035087719298245, 'biological_processes': 0, 'body': 0, 'causation': 0, 'certainty': 0, 'cognitive_processes': 0.05263157894736842, 'colons': 0, 'commas': 0.017543859649122806, 'comparisons': 0, 'conjunctions': 0.07017543859649122, 'dashes': 0, 'death': 0, 'differentiation': 0, 'discrepancies': 0.017543859649122806, 'drives': 0.03508771929824561, 'exclamations': 0, 'family': 0, 'feel': 0, 'female': 0, 'filler_words': 0, 'focus_future': 0, 'focus_past': 0, 'focus_present': 0.14035087719298245, 'friends': 0.017543859649122806, 'function_words': 0.543859649122807, 'health': 0, 'hear': 0, 'home': 0, 'i': 0.03508771929824561, 'impersonal_pronouns': 0.03508771929824561, 'informal_language': 0, 'ingestion': 0, 'insight': 0, 'interrogatives': 0.017543859649122806, 'leisure': 0.14035087719298245, 'male': 0, 'money': 0, 'motion': 0.05263157894736842, 'negations': 0, 'negative_emotion_words': 0, 'netspeak': 0, 'nonfluencies': 0, 'numbers': 0, 'other_grammar': 0.2807017543859649, 'other_punctuation': 0, 'parentheses': 0, 'perceptual_processes': 0.017543859649122806, 'periods': 0.08771929824561403, 'personal_concerns': 0.14035087719298245, 'personal_pronouns': 0.03508771929824561, 'positive_emotion_words': 0.05263157894736842, 'power': 0, 'prepositions': 0.10526315789473684, 'pronouns': 0.07017543859649122, 'quantifiers': 0.05263157894736842, 'question_marks': 0, 'quotes': 0, 'relativity': 0.17543859649122806, 'religion': 0, 'reward': 0.017543859649122806, 'risk': 0, 'sad_words': 0, 'see': 0.017543859649122806, 'semicolons': 0, 'sexual': 0, 'she_he': 0, 'social': 0.03508771929824561, 'space': 0.10526315789473684, 'swear_words': 0, 'tentative': 0.03508771929824561, 'they': 0, 'time': 0.017543859649122806, 'time_orientation': 0.14035087719298245, 'verbs': 0.19298245614035087, 'we': 0, 'work': 0, 'you': 0}}}, 'sallee': {'counts': {'emotions': {'admiration': 5, 'amusement': 0, 'anger': 0, 'boredom': 0, 'calmness': 0, 'curiosity': 0, 'desire': 0, 'disgust': 0, 'excitement': 0.375, 'fear': 0, 'gratitude': 2, 'joy': 6.375, 'love': 5, 'pain': 0, 'sadness': 0, 'surprise': 0}, 'goodfeel': 13.375, 'ambifeel': 0, 'badfeel': 0, 'emotionality': 13.375, 'sentiment': 13.375, 'non_emotion': None}, 'scores': {'emotions': {'admiration': 0.3333333333333333, 'amusement': 0, 'anger': 0, 'boredom': 0, 'calmness': 0, 'curiosity': 0, 'desire': 0, 'disgust': 0, 'excitement': 0.03614457831325301, 'fear': 0, 'gratitude': 0.16666666666666666, 'joy': 0.3893129770992366, 'love': 0.3333333333333333, 'pain': 0, 'sadness': 0, 'surprise': 0}, 'goodfeel': 0.2015065913370998, 'ambifeel': 0, 'badfeel': 0, 'emotionality': 0.2015065913370998, 'sentiment': 0.6541600137038615, 'non_emotion': 0.7984934086629002}, 'emotion_word_count': 4}}]}
js = resp.json()
df = pd.json_normalize(js['results'][0])
df.columns
Index(['response_id', 'request_id', 'language', 'version',
'summary.word_count', 'summary.words_per_sentence',
'summary.sentence_count', 'summary.six_plus_words', 'summary.emojis',
'summary.emoticons',
...
'sallee.scores.emotions.pain', 'sallee.scores.emotions.sadness',
'sallee.scores.emotions.surprise', 'sallee.scores.goodfeel',
'sallee.scores.ambifeel', 'sallee.scores.badfeel',
'sallee.scores.emotionality', 'sallee.scores.sentiment',
'sallee.scores.non_emotion', 'sallee.emotion_word_count'],
dtype='object', length=150)
df.iloc[0]
response_id d1382f42-5c28-4528-ab2e-81b80ba185e2
request_id req-1
language en
version v1.0.0
summary.word_count 57
...
sallee.scores.badfeel 0
sallee.scores.emotionality 0.202
sallee.scores.sentiment 0.654
sallee.scores.non_emotion 0.798
sallee.emotion_word_count 4
Name: 0, Length: 150, dtype: object
I've got numpy array with shape of (3, 50):
data = np.array([[0, 3, 0, 2, 0, 0, 1, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 0,
0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 1, 0, 0, 7, 0, 0, 0, 0,
1, 1, 2, 0, 0, 2],
[0, 0, 0, 0, 0, 3, 0, 1, 6, 1, 1, 0, 0, 0, 0, 2, 0, 0, 1, 0, 1, 0,
3, 0, 0, 0, 0, 0, 0, 5, 2, 2, 2, 1, 0, 0, 1, 0, 1, 3, 2, 0, 0, 0,
0, 0, 2, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0]])
and the following column names:
new_cols = [f'description_word_{i+1}_count' for i in range(50)]
I'm trying to add new columns in already existing dataframe in such way:
df[new_cols] = data
but get the error:
KeyError: "None of [Index(['description_word_1_count',
'description_word_2_count',\n 'description_word_3_count',
'description_word_4_count',\n 'description_word_5_count',
'description_word_6_count',\n 'description_word_7_count',
'description_word_8_count',\n 'description_word_9_count',
'description_word_10_count',\n 'description_word_11_count',
'description_word_12_count',\n 'description_word_13_count',
'description_word_14_count',\n 'description_word_15_count',
'description_word_16_count',\n 'description_word_17_count',
'description_word_18_count',\n 'description_word_19_count',
'description_word_20_count',\n 'description_word_21_count',
'description_word_22_count',\n 'description_word_23_count',
'description_word_24_count',\n 'description_word_25_count',
'description_word_26_count',\n 'description_word_27_count',
'description_word_28_count',\n 'description_word_29_count',
'description_word_30_count',\n 'description_word_31_count',
'description_word_32_count',\n 'description_word_33_count',
'description_word_34_count',\n 'description_word_35_count',
'description_word_36_count',\n 'description_word_37_count',
'description_word_38_count',\n 'description_word_39_count',
'description_word_40_count',\n 'description_word_41_count',
'description_word_42_count',\n 'description_word_43_count',
'description_word_44_count',\n 'description_word_45_count',
'description_word_46_count',\n 'description_word_47_count',
'description_word_48_count',\n 'description_word_49_count',
'description_word_50_count'],\n dtype='object')] are in the
[columns]"
Also I don't know where it finds a '\n' symbols in my column names.
At the same time creating a new dataframe with the data is OK:
new_df = pd.DataFrame(data=data, columns=new_cols)
Does anyone know what is causing the error?
Suppose you have a df like this:
df = pd.DataFrame({'person': [1,1,1], 'event': ['A','B','C']})
You can add new columns like this:
import pandas as pd
import numpy as np
data = np.array([[0, 3, 0, 2, 0, 0, 1, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 0,
0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 1, 0, 0, 7, 0, 0, 0, 0,
1, 1, 2, 0, 0, 2],
[0, 0, 0, 0, 0, 3, 0, 1, 6, 1, 1, 0, 0, 0, 0, 2, 0, 0, 1, 0, 1, 0,
3, 0, 0, 0, 0, 0, 0, 5, 2, 2, 2, 1, 0, 0, 1, 0, 1, 3, 2, 0, 0, 0,
0, 0, 2, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0]])
new_cols = [f'description_word_{i+1}_count' for i in range(50)]
df[new_cols] = pd.DataFrame(data, index=df.index)
I think the problem is that you are using a syntax to create series, when you actually need to create several series. In other words, a dataframe.
I am working with an API that is sending me a JSON response containing an NSArray of bytes represented with integers.
See link for example (bottom example).
http://developer.yodlee.com/Indy_FinApp/Aggregation_Services_Guide/Aggregation_REST_API_Reference/getMFAResponse
Here is a snippet of that example:
{
"isMessageAvailable":true,
"fieldInfo":{
"responseFieldType":"text",
"imageFieldType":"image",
"image":[
66,
77,
58,
116,
0,
0,
0,
0,
0,
0,
54,
0,
0,
0,
40,
0,
0,
0,
-91,
0,
0,
0,
45,
0,
0,
0,
1,
0,
32,
0,
0,
0,
0,
0,
4,
116,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
-1,
-1,
-1,
0,
-1,
-1,
-1,
0,
-1,
-1,
-1,
0,
-1,
-1,
-1,
0,
-1,
-1,
-1,
0,
-1,
-5,
-1,
0,
-1,
-1,
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],
"minimumLength":-1,
"maximumLength":-1,
"displayString":"Please enter the text shown in the image"
},
"timeOutTime":99900,
"itemId":10014925,
"retry":false
}
NSJSONSerialization will actually convert this to an NSArray of NSNumbers I believe.
I am confused as to how I would convert an NSArray of NSNumbers to image data.
I looked around on google and found examples in PHP, Java and C++, but nothing specifically for Objective-C/Cocoa.
Does anyone know how to do this?
Something like this should work:
NSArray *jsonBytes = ...
char *bytes = malloc(jsonBytes.count);
NSUInteger index = 0;
for (NSNumber *byteNumber in jsonBytes) {
bytes[index++] = [byteNumber charValue];
}
NSData *data = [NSData dataWithBytesNoCopy:bytes
length:jsonBytes.count
freeWhenDone:YES];
//maybe also specify the scale
UIImage *image = [UIImage imageWithData:data];