\n",
"\n",
"\n",
"#### 1.1.1 Anaconda környezet\n",
"\n",
"\n",
"Anaconda esetén a telepítés az Anaconda Prompt megnyitásával zajlik. Ahhoz, hogy az Anaconda ne kérdezzen rá minden alkalommal, hogy biztosan telepíteni szeretnénk-e, célszerű futtatni a\n",
"```shell\n",
"conda config --set always_yes true\n",
"```\n",
"sort. Ezután minden beírt parancs végre fog hajtódni rákérdezés nélkül.\n",
"\n",
"A telepítéshez a következő sort kell beírni:\n",
"\n",
"```shell\n",
"conda install pip && pip install pysprint[optional]\n",
"\n",
"(ELAVULT) conda install -c ptrskay pysprint -c conda-forge\n",
"```\n",
"\n",
"\n",
"Anaconda Promptban a következő paranccsal le is ellenőrizhető, hogy telepítve van-e a csomag.\n",
"\n",
"```shell\n",
"conda list pysprint\n",
"```\n",
"\n",
"Ez valami hasonlót ad vissza, ha telepítve van:\n",
"```shell\n",
" packages in environment at C:\\Users\\lp\\miniconda3:\n",
"\n",
" Name Version Build Channel\n",
"pysprint 0.13.1 py37_0 ptrskay\n",
"```\n",
"Ha valamilyen okból nem sikerült, akkor az előbbi parancs egy üres sort fog visszaadni.\n",
"\n",
"A csomag frissítése ugyancsak az Anaconda Prompttal történik (fontos lehet, mivel a jövőben még frissíteni fogom a programot). \n",
"\n",
"A frissíteni a köv. paranccsal lehetséges:\n",
"```shell\n",
"python -m pip install --upgrade pysprint\n",
"(ELAVULT) conda update pysprint\n",
"```\n",
"\n",
"A frissítésen és telepítésen kívül az Anaconda Prompt nem szükséges.\n",
"\n",
"#### 1.1.2 Hagyományos Python környezet, PIP\n",
"\n",
"Hagyományos Python környezetet használva a telepítés a Parancssorban történik:\n",
"```shell\n",
"python -m pip install pysprint\n",
"```\n",
"A frissítéshez tartozó parancs pedig:\n",
"```shell\n",
"python -m pip install --upgrade pysprint\n",
"```\n",
"\n",
"\n",
"### 1.2 A development verzió telepítése, contributing\n",
"\n",
"Új felhasználók számára a Git verziókövető szoftver ismerete lehet a legnehezebben érthető. Nagyon gyorsan bonyolulttá válhat, de az itt leírtak segíthetnek a folyamat könnyű és gondmentes végrehajtásában.\n",
"A kód a [GitHub](https://github.com/Ptrskay3/PySprint)-on tárolódik. Ahhoz, hogy a program fejlesztésében részt lehessen venni, szükség van egy [GitHub felhasználóra](https://github.com/signup/free), illetve a [Git](https://git-scm.com/) szoftver telepítésére. \n",
"\n",
"Néhány forrás, ahol részletesebben olvashatunk a Git-ről:\n",
"* [GitHub instructions](https://docs.github.com/en/github/getting-started-with-github/set-up-git)\n",
"* [NumPy documentation](https://numpy.org/doc/stable/dev/index.html)\n",
"\n",
"Az első lépés ezek után egy saját \"fork\" készítése. A [projekt oldalán](https://github.com/Ptrskay3/PySprint) a jobb felső sarokban a Fork gomb megnyomása után egy másolatot készít a saját felhasználónknak. Ezután a parancssort használva a következőket kell beírnunk:\n",
"```shell\n",
"git clone https://github.com/your-user-name/pysprint.git pysprint-yourname\n",
"cd pysprint-yourname\n",
"git remote add upstream https://github.com/Ptrskay3/PySprint\n",
"```\n",
"Itt természetesen a `your-user-name` helyére a saját Git felhasználónk nevét kell helyettesíteni. Ez a gépünkre letölti a megfelelő fájlokat a `pysprint-yourname` könyvtárba és összekapcsolja azt az \"upstream\" kóddal.\n",
"\n",
"Töltsük le a [Rust programozási nyelvet](https://www.rust-lang.org/tools/install), majd a terminálban váltsunk a nightly verziójára:\n",
"```shell\n",
"rustup toolchain install nightly\n",
"rustup override set nightly\n",
"```\n",
"\n",
"Ezután a megfelelő Python környezetet kell beállítanunk. Anaconda esetén ez egyszerű:\n",
"\n",
"A terminált használva először győződjünk meg, hogy az Anaconda legfrissebb verziója van-e telepítve (`conda update conda`). Ez követően navigáljunk el a `pysprint-yourname` könyvtárba, ahova a program letöltődött. Itt a parancssorban futtassuk a következőket:\n",
"```shell\n",
"# a környezet létrehozása és aktiválása\n",
"conda env create -f environment.yml\n",
"conda activate pysprint-dev\n",
"\n",
"# a program telepítése a letöltött fájlokból\n",
"python -m pip install -e .[optional]\n",
"```\n",
"Ekkor a lokális telepítésből már tudjuk importálni a csomagot:\n",
"```shell\n",
"python # interpreter elindítása\n",
">>> import pysprint as ps\n",
">>> print(ps.__version__)\n",
"0.13.1\n",
"```\n",
"\n",
"A Git segítségével különböző ágakkal (branch) lehet dolgozni. Ezek közül a fő ág a `master`. A `master` branch-en csakis a production-ready kódot szeretnénk tartani, így ott sose dolgozzunk. \n",
"Először győződjünk meg, hogy a legfrissebb verzió van nálunk:\n",
"```shell\n",
"git checkout master\n",
"git pull upstream master\n",
"```\n",
"Készítsünk egy új ágat:\n",
"```shell\n",
"git checkout -b my-new-feature\n",
"```\n",
"Ezzel létrehoztuk a `my-new-feature` ágat és át is váltottunk rá. Ezután elkezdhetjük a kódot írni.\n",
"\n",
"Ha megváltoztattunk bizonyos fájlokat, akkor a `git status` paranccsal ellenőrizhetjük a fájlok státuszát.\n",
"Mielőtt a változtatásokat véglegesítenénk előbb mindenképpen futtassuk le az automatizált teszteket, hogy megbizonyosodjuk arról, hogy nem rontottunk el már meglévő funkciókat. Ezt két módon lehetséges a gyökérkönyvtárban:\n",
"```\n",
"cd pysprint/tests\n",
"pytest -vv\n",
"```\n",
"vagy\n",
"```\n",
"tox\n",
"```\n",
"Amennyiben ezek nem adnak hibát akkor a változtatásokat a saját \"fork\"-ba fel is tölthetjük a következő módon:\n",
"```shell\n",
"git add -A\n",
"git commit -m \"some description about what I've done\"\n",
"git push origin my-new-feature\n",
"```\n",
"\n",
"A manuális környezet beállítása helyett használható a Docker is. A projekt biztosít ehhez a főkönyvtárában egy `Dockerfile`-t."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3 Első lépések\n",
"Szigorúan véve nem szükséges a `numpy` és `matplotlib` importálás ahhoz, hogy a csomag működjön, de a biztonság kedvéért ajánlott előtte azokat is meghívni. Ez után a csomag importálása szokásos módon az\n",
"```python\n",
"import pysprint as ps\n",
"```\n",
"sorral történik. A `ps` rövidítést én vezettem be, ezt tartottam logikusnak."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import pysprint as ps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A verzió számát a python csomagoknál megszokott \n",
"```python\n",
"ps.__version__\n",
"```\n",
"rövidítéssel érhetjük el. A későbbiekben fontos lesz, mivel néhány kisebb hiba javításra kerülhet."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"verzió: e29766d-dirty\n",
"szerző: Leéh Péter\n"
]
}
],
"source": [
"print('verzió: ', ps.__version__)\n",
"print('szerző: ', ps.__author__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A saját telepítésemnél a verzió száma más logikán alapul, normál telepítésnél rendes verziószámot fog kiírni, pl. *0.13.1*.\n",
"\n",
"### 1.4 Hibák jelentése\n",
"\n",
"A programhoz tartozik a `print_info` függvény, amivel minden fontos információt kiír a környezetről, ami a problémák megoldásánál hasznos lehet. Hibát jelenteni a szoftver [GitHub oldalán](https://github.com/Ptrskay3/PySprint/issues) az *issue* fül alatt lehetséges, vagy a *leeh123peter@gmail.com* e-mail címen."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"PYSPRINT ANALYSIS TOOL\n",
"\n",
" SYSTEM\n",
"----------------------\n",
"python : 3.7.6.final.0\n",
"python-bits: 64\n",
"OS : Windows\n",
"OS-release : 10\n",
"Version : 10.0.18362\n",
"machine : AMD64\n",
"processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel\n",
"byteorder : little\n",
"\n",
" DEPENDENCY\n",
"----------------------\n",
"pysprint : e29766d-dirty\n",
"numpy : 1.19.4\n",
"scipy : 1.5.4\n",
"matplotlib : 3.3.3\n",
"pandas : 1.1.4\n",
"pytest : 6.1.1\n",
"lmfit : 1.0.1\n",
"numba : 0.50.1\n",
"IPython : 7.12.0\n",
"jinja2 : 2.11.2\n",
"dask : 2.29.0\n",
"sklearn : 0.23.2\n",
"\n",
" ADDITIONAL\n",
"----------------------\n",
"Conda-env : False\n",
"IPython : True\n",
"Spyder : False\n",
"Rust-ext : True\n"
]
}
],
"source": [
"ps.print_info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.5 Mértékegységek"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Először fontos kikötni a mértékegységeket: hullámhossztartományban *nm*, frekvenciatartományban *PHz* egységekben dolgozik a program. **Minden kiértékelés előtt frekvenciatartományba kell váltani az adatsort.** Az eredmények ezután *fs* (és hatványai) egységekben lesznek kiszámítva."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Szimulációk\n",
"\n",
"Ez egy kevésbé használt funkció, a példák során ezt is fogom használni, így illik ezt is bemutatni.\n",
"A funkció a következőképpen néz ki (az alapértelmezett értékeivel):\n",
"\n",
"```\n",
"ps.Generator(\n",
" start, < -- a spektrum kezdőértéke, hullámhossztartomány esetén nm, frekvenciatartomány esetén PHz\n",
" stop, < -- a spektrum végértéke, hullámhossztartomány esetén nm, frekvenciatartomány esetén PHz\n",
" center, < -- a központi hullámhossz/frekvencia\n",
" delay=0, < -- a karok közti időbeli késleltetés fs egységekben\n",
" GD=0, \n",
" GDD=0,\n",
" TOD=0, < -- a diszperziós együtthatók\n",
" FOD=0,\n",
" QOD=0,\n",
" resolution=0.1, < -- az interferogram felbontása nm egységekben (függetlenül milyen tartományban dolgozunk)\n",
" delimiter=\",\", < -- a mentésnél az alapértelmezett elválasztó\n",
" pulse_width=10, < -- az impulzus félértékszélességét szabályzó paraméter fs egységben (lent tau_p)\n",
" normalize=False, < -- normálja-e az interferogramot (ha igen, akkor a karok spektrumát is visszaadja)\n",
" chirp=0, < -- lineáris csörp paraméter (lent a)\n",
")\n",
"```\n",
"\n",
"A spektrális intenzitás alakja:\n",
"\n",
"\n",
"$\\LARGE{I(\\omega) = C\\cdot e^{\\frac{(\\omega-\\omega_0)^2\\cdot \\tau_p^2}{4 ln2\\sqrt{1 + a^2}}}}$\n",
"\n",
"A használatához először inicializálni kell a `Generator` objektumot, aztán annak függvényében, hogy milyen tartományban szeretnénk generálni az interferogramot két lehetőség van:\n",
"* `ps.Generator.generate_freq()` - a frekvenciatartománybeli generálás\n",
"* `ps.Generator.generate_wave()` - a hullámhossztartománybeli generálás\n",
"* `ps.Generator.generate()` - automatikusan eldönti, hogy milyen tartományban generálja\n",
"\n",
"A következőkben két interferogramot generálok, az egyiket frekvenciatartományban (1 és 4 *PHz* között 2.355 *PHz* központi frekvenciával fog egy harmadrendű diszperziót tartalmazó interferogramot generálni 100 *fs* karok közti késleltetésnél), illetve a másikat hullámhossztartományban (hasonló paraméterekkel, csak 400 és 1000 *nm* között 800 *nm* központi hullámhosszal)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"f = ps.Generator(1, 4, 2.355, 100, TOD=500, pulse_width=5) # <-- itt még nem számolja ki a spektrumot\n",
"f.generate_freq() # < -- ezzel a sorral már kiszámolja a spektrumot\n",
"\n",
"g = ps.Generator(400, 1000, 800, 100, TOD=500, normalize=True) # <-- hasonlóan itt még nem számolja ki a spektrumot\n",
"g.generate_wave() # < -- ezzel a sorral már kiszámolja a spektrumot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ezzel létrehoztuk a `g` és `f` generátor objektumokat és generáltuk is a hozzájuk tartozó spektrumokat. Néhány funkciója a `Generator` objektumoknak:\n",
"* `ps.Generator.show()` <-- mutassa a generált interferogramot\n",
"* `ps.Generator.phase_graph()` <-- mutassa a generált interferogramot együtt a spektrális fázisfüggvénnyel\n",
"* `ps.Generator.save(name)` <-- elmenti a spektrumot *name* névvel egy txt fájlba\n",
"* `ps.Generator.data` <-- adja vissza a generált adatokat np.ndarray-ként\n",
"\n",
"Példaként néhányat ezek közül meghívok:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A generált adatok eléréséhez a `ps.Generator.data` függvényt használhatjuk. Ha normáljuk az interferogramot, akkor ez vissza fogja adni a karok spektrumait is. Minden visszaadott érték `np.ndarray` típusú."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"gx, gy, gref, gsam = g.data # a g normált volt, ezért a referencia- és tárgynyaláb spektrumát is visszaadja"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"fx, fy = f.data # az f pedig nem"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"print(type(fy))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Alapelemek és koszinusz-függvény illesztéses módszer, CosFitMethod\n",
"\n",
"\n",
"Néhány művelet minden kiértékelési módszernél hasznos lehet, (ilyenek pl. a hullámhossztartományból frekvenciatartományba váltás vagy fordítva, normálás, fontos részek kivágása, stb.) ezért ezt minden objektum eléri. Erről egy külön munkafüzetet készítettem, ahol részletesen leírok mindent. \n",
"Ez a következő szekcióban érhető el.\n",
"\n",
"A támogatott kiértékelési eljárások:\n",
"\n",
"* `ps.MinMaxMethod` - Minimum-maximum módszer\n",
"* `ps.CosFitMethod` - Fázismodulált koszinusz-függvény illesztéses módszer\n",
"* `ps.FFTMethod` - Fourier-transzformációs módszer\n",
"* `ps.WFTMethod` - Ablakolt Fourier-transzformációs módszer (a 0.12.1 verziótól elérhető)\n",
"* `ps.SPPMethod` - Állandó fázisú pont módszere\n",
"\n",
"Az alábbi bevezető példában a `CosFitMethod`-ot fogom használni. Kétféleképpen tölthetjük be az interferogramot: fájlból töltjük be az adatokat, vagy `np.ndarray`-ban már Pythonban be van töltve nekünk (pl. a szimulációt használtuk) a szükséges spektrum."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\pyt\\pysprint\\pysprint\\core\\bases\\dataset.py:207: PySprintWarning: Extreme values encountered during normalization.\n",
"Nan values: 105\n",
"Inf values: 1\n",
" PySprintWarning\n"
]
}
],
"source": [
"# 1. mód: np.ndarrayból töltjük be\n",
"a = ps.CosFitMethod(gx, gy, gref, gsam) # < -- ezek a fenti szimulációból származnak\n",
"\n",
"# vagy röviden:\n",
"# a = ps.CosFitMethod(*g.data)\n",
"\n",
"# 2. mód: fájlból töltjük be\n",
"b = ps.CosFitMethod.parse_raw(\n",
" 'datasets/ifg.trt',\n",
" 'datasets/ref.trt',\n",
" 'datasets/sam.trt',\n",
" skiprows=8,\n",
" meta_len=6,\n",
" delimiter=\";\",\n",
" decimal=\",\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Az `parse_raw` függvény alapbeállításait a `help(ps.Dataset.parse_raw)` függvénnyel könnyen átnézhetjük. Például egy tizedespontokat használó, vesszővel elválasztott adatokat tartalmazó fájlt a következőképpen kellene helyesen betölteni:\n",
"```python\n",
"mm2 = ps.MinMaxMethod.parse_raw('ifg.txt', 'ref.txt', 'sam.txt', decimal='.', sep=',', skiprows=0, meta_len=0) \n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method parse_raw in module pysprint.core.bases.dataset:\n",
"\n",
"parse_raw(filename, ref=None, sam=None, skiprows=0, decimal='.', sep=None, delimiter=None, comment=None, usecols=None, names=None, swapaxes=False, na_values=None, skip_blank_lines=True, keep_default_na=False, meta_len=1, errors='raise', callback=None, parent=None, **kwargs) method of pysprint.core.bases._dataset_base._DatasetBase instance\n",
" Dataset object alternative constructor.\n",
" Helps to load in data just by giving the filenames in\n",
" the target directory.\n",
" \n",
" Parameters\n",
" ----------\n",
" filename: `str`\n",
" base interferogram\n",
" file generated by the spectrometer\n",
" ref: `str`, optional\n",
" reference arm's spectra\n",
" file generated by the spectrometer\n",
" sam: `str`, optional\n",
" sample arm's spectra\n",
" file generated by the spectrometer\n",
" skiprows: `int`, optional\n",
" Skip rows at the top of the file. Default is `0`.\n",
" decimal: `str`, optional\n",
" Character recognized as decimal separator in the original dataset.\n",
" Often `,` for European data.\n",
" Default is `.`.\n",
" sep: `str`, optional\n",
" The delimiter in the original interferogram file.\n",
" Default is `,`.\n",
" delimiter: `str`, optional\n",
" The delimiter in the original interferogram file.\n",
" This is preferred over the `sep` argument if both given.\n",
" Default is `,`.\n",
" comment: `str`, optional\n",
" Indicates remainder of line should not be parsed. If found at the beginning\n",
" of a line, the line will be ignored altogether. This parameter must be a\n",
" single character. Default is `'#'`.\n",
" usecols: list-like or callable, optional\n",
" If there a multiple columns in the file, use only a subset of columns.\n",
" Default is [0, 1], which will use the first two columns.\n",
" names: array-like, optional\n",
" List of column names to use. Default is ['x', 'y']. Column marked\n",
" with `x` (`y`) will be treated as the x (y) axis. Combined with the\n",
" usecols argument it's possible to select data from a large number of\n",
" columns.\n",
" swapaxes: bool, optional\n",
" Whether to swap x and y values in every parsed file. Default is False.\n",
" na_values: scalar, str, list-like, or dict, optional\n",
" Additional strings to recognize as NA/NaN. If dict passed, specific\n",
" per-column NA values. By default the following values are interpreted as\n",
" NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’,\n",
" ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’,\n",
" ‘’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘n/a’, ‘nan’, ‘null’.\n",
" skip_blank_lines: bool\n",
" If True, skip over blank lines rather than interpreting as NaN values.\n",
" Default is True.\n",
" keep_default_na: bool\n",
" Whether or not to include the default NaN values when parsing the data.\n",
" Depending on whether na_values is passed in, the behavior changes. Default\n",
" is False. More information available at:\n",
" https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html\n",
" meta_len: `int`, optional\n",
" The first `n` lines in the original file containing the meta\n",
" information about the dataset. It is parsed to be dict-like.\n",
" If the parsing fails, a new entry will be created in the\n",
" dictionary with key `unparsed`.\n",
" Default is `1`.\n",
" errors: string, optional\n",
" Determines the way how mismatching sized datacolumns behave.\n",
" The default is `raise`, and it will raise on any error.\n",
" If set to `force`, it will truncate every array to have the\n",
" same shape as the shortest column. It truncates from\n",
" the top of the file.\n",
" callback : callable, optional\n",
" The function that notifies parent objects about SPP related\n",
" changes. In most cases the user should leave this empty. The\n",
" default callback is only initialized if this object is constructed\n",
" by the `pysprint.SPPMethod` object.\n",
" parent : any class, optional\n",
" The object which handles the callback function. In most cases\n",
" the user should leave this empty.\n",
" kwargs : dict, optional\n",
" The window class to use in WFTMethod. Has no effect while using other\n",
" methods. Must be a subclass of pysprint.core.windows.WindowBase.\n",
"\n"
]
}
],
"source": [
"help(ps.CosFitMethod.parse_raw)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Minden ilyen objektum különleges print funkcióval rendelkezik. Itt néhány alapvető információt jelenít meg magáról, illetve ha fájlból töltöttük be, akkor kiolvas adatokat egy saját dictionary-be."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CosFitMethod\n",
"----------\n",
"Parameters\n",
"----------\n",
"Datapoints: 2633\n",
"Predicted domain: wavelength\n",
"Range: from 360.50000 to 1200.25000 nm\n",
"Normalized: True\n",
"Delay value: Not given\n",
"SPP position(s): Not given\n",
"----------------------------\n",
"Metadata extracted from file\n",
"----------------------------\n",
"{\n",
" \"Average\": \"1 scans\",\n",
" \"Data measured with spectrometer name\": \"1107006U1\",\n",
" \"Integration time\": \"2,00 ms\",\n",
" \"Nr of pixels used for smoothing\": \"0\",\n",
" \"Wave\": \"Sample\",\n",
" \"comment\": \"m_ifg 8,740\",\n",
" \"reference_comment\": \"m_ref 8,740\",\n",
" \"sample_comment\": \"m_sam 8,740\",\n",
" \"unparsed\": [\n",
" [\n",
" \"Timestamp [10 microsec ticks]256373440\"\n",
" ]\n",
" ]\n",
"}\n"
]
}
],
"source": [
"print(b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A saját dictionary a `meta` kulcsszóval érhető el."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'comment': 'm_ifg 8,740',\n",
" 'Integration time': '2,00 ms',\n",
" 'Average': '1 scans',\n",
" 'Nr of pixels used for smoothing': '0',\n",
" 'Data measured with spectrometer name': '1107006U1',\n",
" 'unparsed': [['Timestamp [10 microsec ticks]256373440']],\n",
" 'Wave': 'Sample',\n",
" 'reference_comment': 'm_ref 8,740',\n",
" 'sample_comment': 'm_sam 8,740'}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.meta"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Akár saját információt is hozzáadhatunk a dictionary adattípusnál megszokott módon:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"b.meta['fontos_adat_amit_be_szeretnek_allitani'] = 20"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'comment': 'm_ifg 8,740',\n",
" 'Integration time': '2,00 ms',\n",
" 'Average': '1 scans',\n",
" 'Nr of pixels used for smoothing': '0',\n",
" 'Data measured with spectrometer name': '1107006U1',\n",
" 'unparsed': [['Timestamp [10 microsec ticks]256373440']],\n",
" 'Wave': 'Sample',\n",
" 'reference_comment': 'm_ref 8,740',\n",
" 'sample_comment': 'm_sam 8,740',\n",
" 'fontos_adat_amit_be_szeretnek_allitani': 20}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.meta"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A metaadatok közül talán a comment a legfontosabb, mivel ez az, amit a mérés során mi írunk be. Ezt kiírtani a köv. módon lehet:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"m_ifg 8,740\n"
]
}
],
"source": [
"print(b.meta['comment'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Jupyter Notebookban, illetve Jupyter Labon belül az ilyen objektumoknak - a `print` funkcióhoz hasonló - HTML reprezentációja van:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
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\n",
"
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\n",
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\n",
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\n",
" CosFitMethod\n",
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\n",
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\n",
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\n",
"
\n",
" Parameters\n",
"
\n",
"
\n",
"
\n",
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Datapoints
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2633
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Predicted domain
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wavelength
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Range min
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360.50000 nm
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Range max
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1200.25000 nm
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Normalized
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True
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\n",
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Delay value
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SPP position(s)
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m_ifg 8,740
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Integration time
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2,00 ms
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Average
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1 scans
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Nr of pixels used for smoothing
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0
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Data measured with spectrometer name
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1107006U1
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m_ref 8,740
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m_sam 8,740
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20
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""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Láthattuk a kiíratás során, hogy *Predicted domain: wavelength*. Frekvenciatartományba váltani a `chdomain()` függvénnyel lehet. Ekkor újra kiprintelve már a domain frequency-re vált. Megjegyzés: ez csak akkor működik jól, ha `PHz` és `nm` egységekben dolgozunk, különben rossz lehet a predikció."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"b.chdomain()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `plot()` metódussal meg tudjuk nézni az adatsort. Jupyterben nem szükséges a `plt.show()` meghívása, de máshol az lehet. Itt látható, hogy a számunkra fontos rész kb. 2.0 és 3.75 *PHz* között van, a többit nem kívánjuk használni. A kivágás a `slice()` metódussal lehetséges."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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\n",
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