|
211 | 211 | "gpas.nbytes" |
212 | 212 | ] |
213 | 213 | }, |
| 214 | + { |
| 215 | +"cell_type":"markdown", |
| 216 | +"metadata": {}, |
| 217 | +"source": [ |
| 218 | +"## About data types\n" |
| 219 | + ] |
| 220 | + }, |
| 221 | + { |
| 222 | +"cell_type":"code", |
| 223 | +"execution_count":16, |
| 224 | +"metadata": {}, |
| 225 | +"outputs": [ |
| 226 | + { |
| 227 | +"data": { |
| 228 | +"text/plain": [ |
| 229 | +"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", |
| 230 | +" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", |
| 231 | +" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", |
| 232 | +" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", |
| 233 | +" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=uint16)" |
| 234 | + ] |
| 235 | + }, |
| 236 | +"execution_count":16, |
| 237 | +"metadata": {}, |
| 238 | +"output_type":"execute_result" |
| 239 | + } |
| 240 | + ], |
| 241 | +"source": [ |
| 242 | +"study_minutes = np.zeros(100, np.uint16)\n", |
| 243 | +"study_minutes" |
| 244 | + ] |
| 245 | + }, |
214 | 246 | { |
215 | 247 | "cell_type":"code", |
216 | 248 | "execution_count":null, |
217 | 249 | "metadata": {}, |
218 | 250 | "outputs": [], |
219 | 251 | "source": [] |
220 | 252 | }, |
| 253 | + { |
| 254 | +"cell_type":"code", |
| 255 | +"execution_count":17, |
| 256 | +"metadata": {}, |
| 257 | +"outputs": [ |
| 258 | + { |
| 259 | +"name":"stdout", |
| 260 | +"output_type":"stream", |
| 261 | +"text": [ |
| 262 | +"Variable Type Data/Info\n", |
| 263 | +"------------------------------------\n", |
| 264 | +"gpas ndarray 4: 4 elems, type `float64`, 32 bytes\n", |
| 265 | +"gpas_as_list list n=4\n", |
| 266 | +"np module <module 'numpy' from '/Us<...>kages/numpy/__init__.py'>\n", |
| 267 | +"study_minutes ndarray 100: 100 elems, type `uint16`, 200 bytes\n" |
| 268 | + ] |
| 269 | + } |
| 270 | + ], |
| 271 | +"source": [ |
| 272 | +"%whos" |
| 273 | + ] |
| 274 | + }, |
| 275 | + { |
| 276 | +"cell_type":"code", |
| 277 | +"execution_count":15, |
| 278 | +"metadata": {}, |
| 279 | +"outputs": [ |
| 280 | + { |
| 281 | +"data": { |
| 282 | +"text/plain": [ |
| 283 | +"1440" |
| 284 | + ] |
| 285 | + }, |
| 286 | +"execution_count":15, |
| 287 | +"metadata": {}, |
| 288 | +"output_type":"execute_result" |
| 289 | + } |
| 290 | + ], |
| 291 | +"source": [ |
| 292 | +"60 * 24" |
| 293 | + ] |
| 294 | + }, |
| 295 | + { |
| 296 | +"cell_type":"code", |
| 297 | +"execution_count":18, |
| 298 | +"metadata": {}, |
| 299 | +"outputs": [], |
| 300 | +"source": [ |
| 301 | +"study_minutes[0] = 150" |
| 302 | + ] |
| 303 | + }, |
| 304 | + { |
| 305 | +"cell_type":"code", |
| 306 | +"execution_count":19, |
| 307 | +"metadata": {}, |
| 308 | +"outputs": [], |
| 309 | +"source": [ |
| 310 | +"first_day_minutes = study_minutes[0]" |
| 311 | + ] |
| 312 | + }, |
| 313 | + { |
| 314 | +"cell_type":"code", |
| 315 | +"execution_count":20, |
| 316 | +"metadata": {}, |
| 317 | +"outputs": [ |
| 318 | + { |
| 319 | +"data": { |
| 320 | +"text/plain": [ |
| 321 | +"150" |
| 322 | + ] |
| 323 | + }, |
| 324 | +"execution_count":20, |
| 325 | +"metadata": {}, |
| 326 | +"output_type":"execute_result" |
| 327 | + } |
| 328 | + ], |
| 329 | +"source": [ |
| 330 | +"first_day_minutes" |
| 331 | + ] |
| 332 | + }, |
| 333 | + { |
| 334 | +"cell_type":"code", |
| 335 | +"execution_count":21, |
| 336 | +"metadata": {}, |
| 337 | +"outputs": [ |
| 338 | + { |
| 339 | +"data": { |
| 340 | +"text/plain": [ |
| 341 | +"numpy.uint16" |
| 342 | + ] |
| 343 | + }, |
| 344 | +"execution_count":21, |
| 345 | +"metadata": {}, |
| 346 | +"output_type":"execute_result" |
| 347 | + } |
| 348 | + ], |
| 349 | +"source": [ |
| 350 | +"type(first_day_minutes)" |
| 351 | + ] |
| 352 | + }, |
| 353 | + { |
| 354 | +"cell_type":"code", |
| 355 | +"execution_count":22, |
| 356 | +"metadata": {}, |
| 357 | +"outputs": [], |
| 358 | +"source": [ |
| 359 | +"# TODO: Add 60 minutes to the second day in the study_minutes array\n", |
| 360 | +"study_minutes[1] = 60" |
| 361 | + ] |
| 362 | + }, |
| 363 | + { |
| 364 | +"cell_type":"code", |
| 365 | +"execution_count":23, |
| 366 | +"metadata": {}, |
| 367 | +"outputs": [ |
| 368 | + { |
| 369 | +"data": { |
| 370 | +"text/plain": [ |
| 371 | +"array([150, 60, 80, 60, 30, 90, 0, 0, 0, 0, 0, 0, 0,\n", |
| 372 | +" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", |
| 373 | +" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", |
| 374 | +" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", |
| 375 | +" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", |
| 376 | +" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", |
| 377 | +" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", |
| 378 | +" 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=uint16)" |
| 379 | + ] |
| 380 | + }, |
| 381 | +"execution_count":23, |
| 382 | +"metadata": {}, |
| 383 | +"output_type":"execute_result" |
| 384 | + } |
| 385 | + ], |
| 386 | +"source": [ |
| 387 | +"study_minutes[2:6] = [80, 60, 30, 90]\n", |
| 388 | +"study_minutes" |
| 389 | + ] |
| 390 | + }, |
221 | 391 | { |
222 | 392 | "cell_type":"code", |
223 | 393 | "execution_count":null, |
|
